2025-03-17T08:15:29Z https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/oai

oai:ojs.localhost:article/10413 2023-06-27T17:57:41Z STRING:ART

oai:ojs.localhost:article/5775 2020-11-11T11:06:29Z inference:ART

oai:ojs.localhost:article/6349 2021-02-19T11:26:35Z diskursus:ART

oai:ojs.localhost:article/11224 2022-10-12T12:01:45Z Faktor:ART

oai:ojs.localhost:article/26650 2024-12-07T03:24:58Z RDJE:ART

oai:ojs.localhost:article/13177 2023-01-02T11:39:43Z sosio_ekons:ART

oai:ojs.localhost:article/6873 2021-12-08T14:08:08Z inference:ART

oai:ojs.localhost:article/5855 2020-11-11T11:05:01Z inference:ART

oai:ojs.localhost:article/16925 2023-08-22T11:41:09Z diskursus:ART

oai:ojs.localhost:article/5799 2020-11-11T11:06:30Z inference:ART

oai:ojs.localhost:article/6003 2020-11-11T11:05:02Z inference:ART

oai:ojs.localhost:article/10651 2022-02-21T13:10:48Z Faktor:ART

oai:ojs.localhost:article/13774 2022-07-19T00:07:57Z RDJE:ART

oai:ojs.localhost:article/5796 2021-04-06T13:01:13Z inference:ART

oai:ojs.localhost:article/5586 2021-04-20T14:50:24Z inference:ART

oai:ojs.localhost:article/21007 2024-08-30T10:03:43Z JABE:ART

oai:ojs.localhost:article/10042 2021-07-06T12:04:19Z RDJE:ART

oai:ojs.localhost:article/6030 2021-07-30T16:51:03Z inference:ART

oai:ojs.localhost:article/11327 2022-08-10T13:15:15Z Faktor:ART

oai:ojs.localhost:article/12953 2023-02-04T07:56:20Z Jurnal_Desain:ART

oai:ojs.localhost:article/5761 2020-11-11T11:06:29Z inference:ART

oai:ojs.localhost:article/6795 2021-01-03T12:03:50Z Jurnal_Desain:ART

oai:ojs.localhost:article/23281 2024-08-03T08:15:42Z pkm:ART

oai:ojs.localhost:article/10200 2022-08-05T13:29:38Z Faktor:ART

oai:ojs.localhost:article/6029 2021-07-30T16:54:43Z inference:ART

oai:ojs.localhost:article/11783 2022-10-01T00:11:07Z Deiksis:ART

oai:ojs.localhost:article/6837 2021-04-06T13:01:13Z inference:ART

oai:ojs.localhost:article/6977 2021-02-27T11:18:40Z diskursus:ART

oai:ojs.localhost:article/23184 2024-09-30T06:07:04Z RDJE:ART

oai:ojs.localhost:article/11339 2022-02-03T20:51:33Z Deiksis:ART

oai:ojs.localhost:article/5491 2020-11-11T11:06:29Z inference:ART

oai:ojs.localhost:article/7061 2021-04-12T13:48:34Z alursejarah:ART

oai:ojs.localhost:article/10325 2021-12-24T14:22:10Z Faktor_Exacta:ART

ANALISIS SENTIMEN PENGARUH PEMBELAJARAN DARING TERHADAP MOTIVASI BELAJAR DI MASA PANDEMI MENGGUNAKAN NAIVE BAYES DAN SVM Ariansyah, Ariansyah; Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Kusmira, Mira; UNIVERSITAS NUSA MANDIRI The COVID-19 pandemic in Indonesia has had a huge impact on the education sector. Where it is today, it must implement and adapt a new learning model called online. motivation in learning is very important because it can improve achievements. In this case there are many pros and cons about online learning, many people's opinions on social media, especially Twitter about the influence of online learning on learning motivation. This study aims to analyze the influence between online learning and learning motivation. Public opinion on Twitter is used as a sentiment analysis to find out what people think about online learning on learning motivations whether positive or negative. The data used are tweets in indonesian with the keywords "online learning", "distance learning" and "motivational learning", with the number of datasets as many as 455 tweets are classified into 2 parts namely agreement and disagreement. The classification in this study used naive bayes classification algorithm method and Support Vector Machine (SVM) by preprocessing data using tokenize, transform case, filtering and stemming. Data is processed using rapidminer application. The highest accuracy result of this study was by the classification algorithm method support vector machine (SVM) with accuracy 97.22%, precision 94.72%, recall 100% and error 2.78%. LPPM 2021-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10325 10.30998/faktorexacta.v14i3.10325 Faktor Exacta; Vol 14, No 3 (2021); 100-106 Faktor Exacta; Vol 14, No 3 (2021); 100-106 2502-339X 1979-276X 10.30998/faktorexacta.v14i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10325/4323 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/13706 2022-11-01T15:12:13Z Faktor_Exacta:ART

Perancangan Aplikasi Perbaikan Citra Digital Pada Hasil Screenshot Dengan Menggunakan Metode Multiscale Retinex dan Median Filter Putra, Noga Adi; Universitas 0amulang Amalia, Resti; Universitas Pamulang Advances in technology today cannot be kept away from people's lives, because basically technology is built to provide convenience to the community. One example is in the field of digital images, digital images are a representation of the light intensity function in a discrete field in a two-dimensional plane, digital images can also be a source of information other than text, sound and video. However, sometimes the image can also experience a decrease in quality caused by noise, too contrasting colors, blurring, and so on. A distorted image is usually caused by poor image capture, poor lighting, low pixel resolution on the camera, and poor image capture capabilities. This is what underlies the improvement in digital images, digital image quality improvements can be done by various methods, one of which is the Multiscale Retinex and Median Filter methods. The advantages of the Multiscale Retinex method are that it can increase the brightness and color of digital images, especially those that tend to be dark, and the Median Filter method can reduce the disturbance that often occurs in digital images, namely noise. With the two methods used to improve the image, a digital image will look better with a degree and level of color that tends to be evenly distributed. The process begins by entering the test image with *.jpg file format into the program then processed by the Multiscale Retinex and Median Filter methods, then the initial image can be compared with the resulting image using a histogram. From the research that the author did, it resulted in a digital image processing application that can improve brightness and reduce noise in digital images. LPPM 2022-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13706 10.30998/faktorexacta.v15i3.13706 Faktor Exacta; Vol 15, No 3 (2022); 180-191 Faktor Exacta; Vol 15, No 3 (2022); 180-191 2502-339X 1979-276X 10.30998/faktorexacta.v15i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13706/5147 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/17504 2023-11-10T10:46:54Z Faktor_Exacta:ART

Application of 2DPCA and SOM Algorithms to Identification of Digital Signature Ownership Norhikmah, Norhikmah Signature Is one of the proofs for ratification, one of which is a thesis document, with the development of the when conventional signatures have begin to switch to digital signatures, where digital signatures already have a legal umbrella in Indonesia, currently Covid is still hitting Indonesia, forcing some agencies change the ratification of a document using a digital signature, so that it can provide an opening for falsifying digital signature ratification. Therefore, an application is needed to identify the ownership of a digital signature image, with the first research stage is to collect a digital signature dataset in the form of a signature image or take a dataset from a published legal document, an example of a second stage publication manages the image processing with grayscale first to get extra features and then analyzes the extra feature image using 2DPCA, and identification To get the best matching of image units using the Single Organizing Maps (SOM) method. the results of this study are using the 2DPCA algorithm and SOM to identify ownership of digital signatures, with 84 correct and incorrect test results, from a total dataset of 91 patterns. And get the highest accuracy value of 92.3% at a 20000 translation and a rate of 0.9. LPPM UNIVERSITAS AMIKOM YOGYAKARTA 2023-10-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17504 10.30998/faktorexacta.v16i3.17504 Faktor Exacta; Vol 16, No 3 (2023) Faktor Exacta; Vol 16, No 3 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i3 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17504/6117 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/17504/3475 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/22408 2024-07-17T04:29:56Z Faktor_Exacta:ART

Komparasi Metode Grey GM (1.1) Dan Grey Verhulst Untuk Prediksi Harga Sembako Zahro, Diah Ayu Fatimatus; Universitas PGRI Ronggolawe Muqtadir, Asfan Suryanto, Andik Adi; Universitas PGRI Ronggolawe This research explores the significance of basic commodities (sembako), such as rice and sugar, as essential necessities, with a focus on price fluctuations influenced by factors like seasonal variations and weather conditions. The pressing issue of rising food demand amid Indonesia's population growth is exacerbated by price fluctuations. The study utilizes grey forecasting method, specifically GM (1.1) and grey Verhulst, to predict the prices of basic commodities in East Java. The comparative results indicate that grey Verhulst excels in forecasting the prices of certain commodities, such as Premium Rice, while GM (1.1) proves more effective for the sugar category. This finding comes from an analysis of the ARPE value that shows the accuracy of the model in the price prediction. The research aims to contribute to addressing the challenges of price changes and instability in basic commodity prices influenced by seasonal factors. The lowest error rate for grey Verhulst is 1.9471% for premium rice, with the highest at 64.535% for sugar. For GM (1.1), the lowest error rate is 2.184% for medium rice, and the highest is 6.633% for premium rice. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22408 10.30998/faktorexacta.v17i2.22408 Faktor Exacta; Vol 17, No 2 (2024); 179-187 Faktor Exacta; Vol 17, No 2 (2024); 179-187 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22408/6799 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/22408/4748 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/8630 2021-04-27T14:57:10Z Faktor_Exacta:ART

EVALUASI KUALITAS APLIKASI SISTEM INFORMASI MANAJEMEN KEIMIGRASIAN (SIMKIM) VERSI 2.0 BERBASIS WEB MENGGUNAKAN METODE HUMAN ORGANIZATION TECHNOLOGY FIT (Studi Kasus pada Kantor Imigrasi) Bakri, Arham; Gunadarma University Ridwan, Anggraeni; Universitas Gunadarma Immigration office one of the organization implementing information system integration and automation called the immigration management information system (SIMKIM) Version 2.0 for passport services. An evaluation need to find how the system is implemented, the level of success, as far as to which the system contributes to the organizations that use it. SIMKIM Version 2.0 application have three main component namely human organization and technology. The result of the evaluation indicate technology component include system quality getting value 3,24 (good), information quality getting value 3,09 (good), and service quality getting value 3,21 (good). Human component including system user getting value 3,18 (good), user satisfaction getting value 3,07 (good), and the benefits getting value 3,15 (good), and organization structure getting value 3,22 (good). The overall component SIMKIM Version 2.0 implementation obtained value of 3,16 good interpretation based on HOT Fit method. LPPM 2021-03-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8630 10.30998/faktorexacta.v14i1.8630 Faktor Exacta; Vol 14, No 1 (2021); 14-21 Faktor Exacta; Vol 14, No 1 (2021); 14-21 2502-339X 1979-276X 10.30998/faktorexacta.v14i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8630/3944 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10629 2022-08-17T18:30:09Z Faktor_Exacta:ART

Text Mining of PeduliLindungi Application Reviews on Google Play Store Saputra, Irwansyah Djatna, Taufik; IPB University Siregar, Riki Ruli A.; IPB University Kristiyanti, Dinar Ajeng; Fakultas Teknik dan Informatika, Universitas Multimedia Nusantara Yani, Hasbi Rahma; UIN Imam Bonjol Padang Riyadi, Andri Agung; Universitas Nusa Mandiri Aplikasi PeduliLindungi merupakan aplikasi buatan pemerintah indonesia untuk melakukan pelacakan dan penghentian penyebaran Covid-19. Ulasan terkait aplikasi tersebut tidak seluruhnya baik, hal ini dibuktikan dengan beragamnya peringkat bintang yang diberikan pengguna sehingga terjadinya kesulitan dalam melihat sentimen positif atau negatif terkait aplikasi tersebut. Penelitian ini bertujuan untuk mengklasifikasi ulasan mengenai aplikasi PeduliLindungi kepada dua kelas, yakni sentimen positif dan sentimen negatif. Algoritma klasifikasi yang digunakan adalah klasifikasi Naive Bayes Classifier (NBC). Hasil Menunjukkan Accuracy 85%, Precision 77,7%, Recall 98%, dan F1-Score 86,7%. LPPM 2022-08-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10629 10.30998/faktorexacta.v15i2.10629 Faktor Exacta; Vol 15, No 2 (2022) Faktor Exacta; Vol 15, No 2 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10629/4916 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15900 2023-07-31T11:08:03Z Faktor_Exacta:ART

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization Siswipraptini, Puji Catur Catur; Institut Teknologi PLN Haris, Abdul; Institut Teknologi PLN Sari, Winda Novita The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%. LPPM 2023-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15900 10.30998/faktorexacta.v16i2.15900 Faktor Exacta; Vol 16, No 2 (2023) Faktor Exacta; Vol 16, No 2 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15900/5826 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/21624 2024-05-03T08:21:52Z Faktor_Exacta:ART

Sistem Pendukung Keputusan Penerimaan Fotografer Pada Widya Photography Dengan Metode AHP Isnaini, Kurniati Ismawan, Fiqih; Universitas Indraprasta PGRI Widiyatun, Fita; Universitas Indraprasta PGRI Widya Photography merupakan salah satu usaha di bidang jasa fotografi yang berada di wilayah Jakarta Timur. Adapun rumusan masalah dari penelitian ini adalah ketika melakukan rekruitmen calon fotografer, Widya Photography masih dilakukan secara manual dengan menggunakan media kertas. Hal ini tentunya kurang akurat dan optimal. Penelitian ini bertujuan untuk meningkatkan kualitas pelayanan fotografi pada Widya Photography, maka dibuatlah sistem pendukung keputusan penerimaan fotografer pada Widya Photography dengan Metode Analytical Hierarchy Process (AHP) yaitu metode yang digunakan untuk mengevaluasi dan membuat keputusan multi-kriteria, setelah itu akan dilakukan penilaian melalui hasil perangkingan dari perhitungan perbandingan beberapa kriteria dan sub-kriteria. Hal ini dilakukan agar dalam proses penerimaan fotografer menjadi lebih akurat dan optimal. LPPM 2024-05-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/21624 10.30998/faktorexacta.v17i1.21624 Faktor Exacta; Vol 17, No 1 (2024) Faktor Exacta; Vol 17, No 1 (2024) 2502-339X 1979-276X 10.30998/faktorexacta.v17i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/21624/6611 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/24580 2024-10-30T05:13:35Z Faktor_Exacta:ART

Pemodelan Penentuan Pupuk Menggunakan Metode AHP dan SAW Eliyani, Eliyani; Universitas Budi Luhur Gunawan, Wawan; Universitas Budi Luhur Wahyuningram, Nugroho; Universitas Budi Luhur Triyono, Gandung; Universitas Budi Luhur The dataset used in this study comprises criteria and fertilizer brands for rice, based on assessments conducted by farmers. The criteria were: price (C1), product (C2), quality (C3), quantity (C4), recommendation (C5), effectiveness (C6), and suitability (C7). Each criterion is weighted as Very Good (4), Good (3), Fair (2), and Poor (1). The evaluated fertilizers consisted of 15 brands: Nitrea, Caping Tani, Pertiphos, NPK padi kuning, SP 36, Meroke, Pusri, Nitroku, Ponska, ZA, Urea, and NPK Pak Tani. The assessments were carried out by distributing questionnaires to 50 farmers who shop at Cv. Sari Alam Tani Store, where farmers could select more than one brand of fertilizer. The most chosen fertilizer by the farmers was Urea. This study aims to verify if Urea is indeed the best fertilizer using the AHP and SAW algorithms based on the established criteria. The results indicate that NPK Padi Kuning was ranked first with a score of 1.72, followed by NPK Tawon and NPK Pak Tani with scores of 1.61 and 1.40, respectively. Urea, despite being the most chosen by farmers, ranks fourth with a score of 1.25. LPPM 2024-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/24580 10.30998/faktorexacta.v17i3.24580 Faktor Exacta; Vol 17, No 3 (2024); 323-333 Faktor Exacta; Vol 17, No 3 (2024); 323-333 2502-339X 1979-276X 10.30998/faktorexacta.v17i3 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/24580/7108 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10265 2022-07-14T11:59:08Z Faktor_Exacta:ART

Penerapan QR Code pada Website E-commerce PT. Bravo Satria Perkasa dengan Algoritma Reed-Solomon Code dan Regression Linear Fatimah, Dian; Mercu Buana University Pengelolaan atau manajemen stok yang baik sangat dibutuhkan dalam penjualan, khususnya penjualan dalam skala besar. PT.Barvo Satria Perkasa merupakan perusahaan yang masih melakukan pengelolaan stok barang secara manual, dimana pengeloaan secara manual memiliki resiko selisih stok yang cukup besar. Salah satu solusi untuk mengurangi resiko selisih stok yaitu dengan pengelolaan secara digital. Bentuk dari pengelolaan secara digital yang dituju yaitu penggunaan QR Code pada website E-Commerce dengan algoritma Reed-Solomon Code. Penggunaan algoritma Reed-Solomon Code pada aplikasi berfungsi untuk men-generate QR Code, dimana akan terjadi proses scan barang sebelum dilakukannya proses packing pada penjualan offline, guna menandai produk, baik yang sudah dikirim maupun yang belum dikirim. Selain sebagai error detection Reed-Solomon Code juga bertugas sebagai error correction. LPPM Mercu Buana University 2022-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10265 10.30998/faktorexacta.v15i1.10265 Faktor Exacta; Vol 15, No 1 (2022) Faktor Exacta; Vol 15, No 1 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10265/4783 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15108 2023-03-25T15:10:39Z Faktor_Exacta:ART

KOMPARASI KLASTER PENGANGGURAN TERBUKA DI INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN K-MEAN CLUSTERING Maliqi, Raihan; Univrsitas Nusa Mandiri Falgenti, Kursehi; (Scopus ID 57189267771) Universitas Nusa Mandiri Jakarta Open unemployment is a productive workforce of secondary education and higher education graduates who have not worked at all. The Indonesian Statistics Bureau (BPS) routinely publishes provincial open unemployment (TPT) data per semester. Many studies have analyzed TPT data by exploring new knowledge using data mining methods with cluster analysis techniques. Researchers have investigated the TPT data cluster under normal conditions before the COVID-19 pandemic. This study aims to find new knowledge from TPT data by comparing the TPT data cluster analysis results before the pandemic (2018-2019) with TPT data during the pandemic (2020-2021). Data mining techniques used are cluster analysis and the k-mean algorithm. The cluster analysis results are regional clusters with low and high unemployment rates before and during COVID-19. In addition, another finding is the movement from high to down clusters. Other interesting results are Covid-19 has the most impact on high unemployment in Aceh, North Sumatra, West Sumatra, Riau Islands, DKI Jakarta, West Java, Banten, East Kalimantan, North Sulawesi, Maluku, and West Papua. Anticipatory steps of the high open unemployment rates in 11 provinces, local government could design the program or policy that could support the BLT policy by the central government. LPPM 2023-03-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15108 10.30998/faktorexacta.v16i1.15108 Faktor Exacta; Vol 16, No 1 (2023) Faktor Exacta; Vol 16, No 1 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15108/5523 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/15108/2939 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/13914 2024-05-03T08:13:43Z Faktor_Exacta:ART

Rancangan Sistem Kendali Penyiraman dan Pemupukan untuk Perawatan Tanaman Tembakau pada Pusat Budidaya Di Klaten Jawa Tengah Haryono, Faza Juan; Universitas Indraprasta PGRI Primawati, Alusyanti; Universitas Indraprasta PGRI Awaludin, Aulia Ar Rakhman; Universitas Indraprasta PGRI Penggunaan teknologi IoT mampu menjadi salah satu solusi untuk membantu petani tembakau dalam mengontrol tanaman tembakau. Tujuan dari penelitian ini adalah untuk membangun sebuah sistem dan alat yang dapat mempermudah pekerjaan petani tembakau dalam melakukan penyiraman dan pemupukan pada tanaman tembakau melalui aplikasi android. Metode yang digunakan adalah Waterfall meliputi analisis kebutuhan, desain, implementasi, pengujian, dan perawatan sistem. Pembangunan sistem menggunakan Internet of Things, aplikasi android dan mikrokontroler NodeMCU ESP 8266 untuk mengontrol sensor DHT 11, Soil Moisture YL-69, menyalakan water pump dan mengirimkan data ke aplikasi android untuk menginformasikan temperature dan suhu udara pada tanaman, serta dapat mengontrol penyiraman dan pemupukan. Sistem ini juga dapat menampilkan data grafik mengenai temperature dan kelembaban tanah disetiap waktunya. Pengujian sistem dengan metode blackbox, tahapan dimulai memeriksa fungsi masing-masing komponen sistem sensor untuk mengetahu data pada tanaman tembakau. Semua fungsidapat berjalan dengan baik dan melakukan penyiraman dan pemupukan secara merata. Implementasi dari sistem ini akan mempercepat dan mempermudah pekerjaan para petani tembakau dengan melakukan penyiraman dan pemupukan menggunakan aplikasi android sebagai kontrol IoT yang terhubung ke media tanam tembakau LPPM 2024-05-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13914 10.30998/faktorexacta.v17i1.13914 Faktor Exacta; Vol 17, No 1 (2024); 1-8 Faktor Exacta; Vol 17, No 1 (2024); 1-8 2502-339X 1979-276X 10.30998/faktorexacta.v17i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13914/6602 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/6598 2021-02-18T00:15:09Z Faktor_Exacta:ART

PROTOTYPE SISTEM KENDALI ROBOT ARM GRIPPER MANIPULATOR MENGGUNAKAN FLEX SENSOR DAN MPU6050 BERBASIS INTERNET OF THINGS Nurfaizal, Habib; STMIK Eresha Makhsun, Makhsun Djaksana, Yan Mitha In this sophisticated era a lot of human work that has begun to be done by robots. Robots are used to facilitate work that can not be done by humans, such as moving an item in a dangerous place. One of the robots created to facilitate human work is a robot that has a shape like a human arm called the arm gripper manipulator. Arm gripper manipulator is a robot that has the ability to move like a human arm. The manipulator arm consists of joint, link, and end-effector. This research designed a control system of the robot arm gripper manipulator with 2 modes, gesture mode and IoT mode. The microcontroller used is Arduino Mega 2560 with flex sensor control and MPU 6050 inertia measurement unit sensor in gesture mode and IoT control mode with time average errors in 5 movements is 2.08%. The overall test results of the robot arm gripper manipulator can be controlled with gesture mode and IoT mode.Abstract. In this sophisticated era a lot of human work that has begun to be done by robots. Robots are used to facilitate work that can not be done by humans, such as moving an item in a dangerous place. One of the robots created to facilitate human work is a robot that has a shape like a human arm called the arm gripper manipulator. Arm gripper manipulator is a robot that has the ability to move like a human arm. The manipulator arm consists of joint, link, and end-effector. This research designed a control system of the robot arm gripper manipulator with 2 modes, gesture mode and IoT mode. The microcontroller used is Arduino Mega 2560 with flex sensor control and MPU 6050 inertia measurement unit sensor in gesture mode and IoT control mode with time average errors in 5 movements is 2.08%. The overall test results of the robot arm gripper manipulator can be controlled with gesture mode and IoT mode. LPPM STMIK ERESHA 2021-02-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6598 10.30998/faktorexacta.v13i4.6598 Faktor Exacta; Vol 13, No 4 (2020); 191-199 Faktor Exacta; Vol 13, No 4 (2020); 191-199 2502-339X 1979-276X 10.30998/faktorexacta.v13i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6598/3722 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/6598/1037 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/9841 2021-12-24T14:22:10Z Faktor_Exacta:ART

Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri pada Citra Daun untuk Identifikasi Tumbuhan Obat Harjanti, Trinugi Wira; Sekolah Tinggi Teknologi Informasi NIIT Himawan, Himawan; Sekolah Tinggi Teknologi Informasi NIIT The leaf image identification process depends on the feature extraction results. Each medicinal plant has different shapes and patterns of leaf venation. But for one type of medicinal plants have the same pattern of venation shape and pattern even though the size is different. One of the methods for extraction of leaf image form characteristics is by fractal-based feature extraction. Through fractal can be calculated the value of leaf dimensions and searched parts of leaves that have similarities between one part with other parts. As for the method of extracting the characteristics of venation pattern using B-Spline method. Benefits of research conducted is to help people identifying the types of medicinal plants found, knowing the benefits and ways of brewing. While the research contribution is prototype software application based on information technology that can be used by the people through mobile phones for the identification of medicinal plants. To identify or match the results of feature extraction on the leaf found whether included in the medicinal plant, conducted by Euclidean Distance method. In the experiments we used 1100 data consist of 55 variety of medicinal plants for each 20 samples.The experimental result show that the accuracy of identification using of fractal and b-spline is 85.30%. LPPM 2021-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9841 10.30998/faktorexacta.v14i3.9841 Faktor Exacta; Vol 14, No 3 (2021); 150-159 Faktor Exacta; Vol 14, No 3 (2021); 150-159 2502-339X 1979-276X 10.30998/faktorexacta.v14i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9841/4319 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/14114 2023-01-24T12:06:17Z Faktor_Exacta:ART

Model Matematika pada Penyebaran Penyakit Covid-19 dengan Pengaruh Vaksinasi di DKI Jakarta Ristiawan, Rifki DIRJA; Informatika UNINDRA Solihah, Annisa Ulya; Institut Sains & Teknologi Nasional Coronavirus Disease 2019 or Covid-19 is a disease caused by a coronavirus that attacks the respiratory tract causing high fever, cough, flu, shortness of breath, and sore throat. To see the spread of Covid-19 with the effect of vaccination in DKI Jakarta, this study developed the SIR epidemic model into SVIR by adding a population of vaccinated individuals to prevent the spread of Covid-19. This model assumes that individuals are given the vaccine until the second dose. Individuals vaccinated for two doses can still be infected with Covid-19 if they interact with individuals infected with Covid-19. The population is divided into four classes: the vulnerable individual class, the vaccinated individual class, the Covid-19 infected individual class, and the recovered individual class. Construction of the models starts by making a flow chart of the spread of Covid-19 with the effect of vaccination. This model obtains two equilibrium points, namely the disease-free equilibrium point and the endemic equilibrium point . Analysis of the system's stability around the equilibrium point gives the primary reproduction number . From the analysis results, the system around the disease-free equilibrium point is locally asymptotically stable when . Then a numerical simulation is carried out to provide a geometric picture related to the results that have been analyzed. The simulation results show that when conditions occur, the disease will disappear, and under conditions , the disease will become epidemic. To prevent the spread of Covid-19, efforts can be made to reduce direct contact with infected individuals, implement health protocols and increase the proportion of vaccinated individuals. LPPM 2023-01-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/14114 10.30998/faktorexacta.v15i4.14114 Faktor Exacta; Vol 15, No 4 (2022); 234-242 Faktor Exacta; Vol 15, No 4 (2022); 234-242 2502-339X 1979-276X 10.30998/faktorexacta.v15i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/14114/5351 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/18962 2024-01-09T10:59:48Z Faktor_Exacta:ART

Penerapan Algoritma Sweep dan Particle Swarm Optimization (PSO) sebagai Alternatif Menentukan Rute Distribusi Fauzi, Ilham Saiful; Politeknik Negeri Malang Wardani, Imaniah Bazlina Putra, Indra Lukmana Puspitasari, Peni One aspect of marketing activities is distribution. In the process of distributing goods, it is important to determine the optimal route that minimize mileage and reduce costs. This study aims to provide alternative solutions in determining distribution routes with the shortest distance which has implications for shorter travel times and lower costs. This research adapts the Capacitated Vehicle Routing Problem (CVRP) model with the approach of sweep and Particle Swarm Optimization (PSO) algorithm to determine the route. To generate a comparison route, we use the Nearest Neighbor (NN) algorithm. The result was that 100 agents were divided into 6 clusters and the total distance of the PSO-generated route is 218.115 units or 85.70% of the route distance generated by Nearest Neighbor algorithm. LPPM 2024-01-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/18962 10.30998/faktorexacta.v16i4.18962 Faktor Exacta; Vol 16, No 4 (2023) Faktor Exacta; Vol 16, No 4 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i4 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/18962/6288 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/23169 2024-11-05T04:45:01Z Faktor_Exacta:ART

Pemanfaatan Algoritma K-Means dalam Klasterisasi Gempa Sulawesi Wibowo, Arief; Universitas Budi Luhur Gunawan, Wawan; Universitas Mercu Buana Universitas Budi Luhur Indonesia is a region that frequently experiences earthquakes, especially in the Sulawesi area which has significant active faults. Sulawesi is an area that has quite high seismic intensity, and there are several active faults which are earthquake source zones. This study uses M-Means with a total of 9,710 records starting from 2019-2023 and the attributes consist of event_id, date_time, latitude, longitude, magnitude, mag_type, depth_km, phase_count, azimuth_gap, location, agencydengan. This data processing compares magnitude and depth consisting of 3 clusters, namely 51-132 Km depth with a total of 1,311, 3-50 Km depth with a total of 7,527, 133-300 Km depth with a total of 872, while the process with magnitude, depth and azimuth gap attributes consists of 4 clusters with each cluster respectively 3,957, 1,546, 1,458, and 2,749. By using a different set of input features, this research identifies that the results from 3 clusters or 4 clusters indicate that the province of South Sumatra shows a high level of earthquake proneness and frequent frequency in all clusters with the epicenter of the earthquake being in the Maluku Sea, between South Sulawesi with Southeast Sulawesi, as well as the province of Gorontalo. Based on the results obtained, there is a need for early prevention related to disasters, especially earthquakes that occurred on the island of Sumatra based on earth faults that run through the island.. LPPM 2024-09-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23169 10.30998/faktorexacta.v17i3.23169 Faktor Exacta; Vol 17, No 3 (2024); 228-240 Faktor Exacta; Vol 17, No 3 (2024); 228-240 2502-339X 1979-276X 10.30998/faktorexacta.v17i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23169/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23169/7117 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/9697 2021-08-10T19:34:38Z Faktor_Exacta:ART

Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc Indrayuni, Elly; Universitas Bina Sarana Informatika Nurhadi, Acmad; Universitas Bina Sarana Informatika Kristiyanti, Dinar Ajeng; Universitas Bina Sarana Informatika During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm. LPPM 2021-08-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9697 10.30998/faktorexacta.v14i2.9697 Faktor Exacta; Vol 14, No 2 (2021); 64-71 Faktor Exacta; Vol 14, No 2 (2021); 64-71 2502-339X 1979-276X 10.30998/faktorexacta.v14i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9697/4152 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/13429 2022-08-10T14:49:40Z Faktor_Exacta:ART

PENINGKATAN KUALITAS PRODUK NORMAL NOODLE DENGAN MENGGUNAKAN METODE SIX SIGMA DAN FUZZY FMEA Dwi Satya, Ririn Regiana Nurdeni, Nurdeni; Universitas Indraprasta PGRI PT Indofood CBP Sukses Makmur Tbk is one of the companies engaged in the food sector, namely producing instant noodles. Preliminary research shows that PT Indofood Tbk Suskes Makmur Tbk has products that are not suitable and are formed as a result of sampling the production process, sometimes even exceeding the standards set by the company. The four production processes are cutting, frying, cooling and wrapping. In the four processes produced a number of different defective products, namely: In the cutting process the defective products produced were 30,586 pcs, in the frying process as many as 21,569 pcs, in the cooling process as many as 11,735 pcs and in the wrapping process as many as 42,000 pcs. %. In improving the quality, Six sigma and fuzzy FMEA methods are used. From the results of calculations using the conventional FMEA method and from the results of calculations with fuzzy logic for the value of FRPN using MATLAB software, it has different results, where the highest FRPN value is failure mode 1 (F1) or a risk factor for product defects because many noodle blocks are tucked away which can result in material wasteful with a value of 150 as a rating of 1. With this method, it is hoped that using the Six sigma method and fuzzy FMEA the company can improve quality and reduce the percentage risk of defects in the production process. LPPM 2022-08-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13429 10.30998/faktorexacta.v15i2.13429 Faktor Exacta; Vol 15, No 2 (2022) Faktor Exacta; Vol 15, No 2 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/13429/4921 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/17186 2023-07-31T12:02:22Z Faktor_Exacta:ART

Clustering Indonesian Provinces on Prevalence of Stunting Toddlers Using Agglomerative Hierarchical Clustering Wulandari, Septian; Universitas Indraprasta PGRI Stunting is a chronic nutritional problem caused by a lack of nutritional intake in toddlers. Indonesia is the 5th country with the highest cases of toddler nutrition experiencing stunting at 30.8% in 2018. The current problem, in Indonesia, is providing complete immunization and fulfilling child nutrition in each province is still low. Data obtained from 2018 to 2022 still toddlers who are malnourished and obese and there is no province grouping based on characteristics such as malnutrition, obesity, short toddlers, and complete basic immunization. Clustering is grouping objects into a group so that one cluster contains objects that are similar and different from other objects in other clusters. The agglomerative hierarchical clustering method can classify provinces based on the characteristics that cause stunting so that it can be used as a basis for early prevention for the Indonesian government to tackle stunting and can reduce stunting growth rates which continue to increase and can experience a decline. The agglomerative hierarchical clustering method used is the Average Linkage and Ward's algorithms with the data used is the prevalence of stunting taken in 34 provinces in Indonesia with 11 data attributes. The results of this study are that there are two clusters, namely Cluster 1 which has a relatively high prevalence of stunting with members of 13 provinces, and Cluster 2 which has a relatively low prevalence of stunting with members of 21 provinces. The highest cophenetic correlation value is in Ward's algorithm with a value of 0.8399978. So, it can be said that Ward's algorithm is better than the Average Linkage algorithm in clustering provinces in Indonesia on the prevalence of stunting toddlers. LPPM 2023-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17186 10.30998/faktorexacta.v16i2.17186 Faktor Exacta; Vol 16, No 2 (2023) Faktor Exacta; Vol 16, No 2 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17186/5831 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/20768 2024-07-17T04:29:56Z Faktor_Exacta:ART

Implementasi Metode Simple Additive Weighting Pada Perancangan Sistem Penilaian Reseller di Showroom Aska Motor Garage Ananda, Rizki Yulianingsih, Yulianingsih; Universitas Indraprasta PGRI Megiati, Yunita Endra; Universitas Indraprasta PGRI Aska Motor Garage Showroom is a company engaged in motorcycle sales. As a sales-oriented business, Aska Motor Garage needs a strategy to boost its sales. One of the strategies that can be implemented is by establishing partnerships with resellers through the provision of rewards in the form of incentives. Therefore, there is a need for an objective and measurable assessment system. The Simple Additive Weighting method is one of the techniques used to determine the best value based on criteria and weights that can be customized according to the partners' needs, and it is considered quite appropriate for use in this research. The result of this system design is a decision support system that provides information about the assessment of the top resellers, using four supporting criteria, including monthly sales, innovation, work quality, and adherence to target pricing. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20768 10.30998/faktorexacta.v17i2.20768 Faktor Exacta; Vol 17, No 2 (2024); 141-151 Faktor Exacta; Vol 17, No 2 (2024); 141-151 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20768/6795 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/27733 2025-02-25T08:18:00Z Faktor_Exacta:ART

Sistem Informasi Manajemen Pemesanan Produk RIZAL Wedding Organizer Berbasis Web Mayanti, Rina; Universitas Indraprasta PGRI Pratiwi, Sekar Ageng; Universitas Indraprasta PGRI Hidayat, M Irfan; Universitas Indraprasta PGRI Zana, Sanusi Ibnu; Universitas Indraprasta PGRI Rizal Wedding Organizer in promotion and order wedding organizer process still get trouble, such as reservation services need time and difficulty in order, sometimes get error in processing reservation wedding organizer data, and limited promotion package information. The purpose of this research is to build a Management Information System(MIS)ofWeddingOrganizer(WO)withSundanesetraditionalthemebasedonwebsitein Rizal Wedding Organizer. The Agile Extreme Programming model used to build the system, while website programming uses the Laravel framework and MySQL database. The Management Information System of Wedding Organizer (MIS-O) produces information and promotional media for customers, thusfacilitatingthetransactionprocessfororderingWOservicepackagesaccording to the Sundanese traditional wedding theme they choose. Vendors also find it easy to manage WO service package data on pre-order transactions process. LPPM Universitas Indraprasta PGRI 2025-02-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/27733 10.30998/faktorexacta.v17i4.27733 Faktor Exacta; Vol 17, No 4 (2024) Faktor Exacta; Vol 17, No 4 (2024) 2502-339X 1979-276X 10.30998/faktorexacta.v17i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/27733/7409 Copyright (c) 2025 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/11816 2022-07-14T12:20:25Z Faktor_Exacta:ART

Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes dan Artificial Neural Network : Studi Kasus Fakultas Teknik UNIS Tangerang Romlah, Ummu Habibah; Universitas Tangerang Raya Solichin, Achmad; Universitas Budi Luhur The faculty of engineering has 4(four) studies programs namely, informatics engineering, civil engineering, industrial engineering, chemical engineering. The number of lecturers and students owned by the Faculty of Engineering based on PDDikti Year1 2019/2020 reporting data is 41 permanent lecturers and 750 students. The problems faced by the Faculty of Engineering UNIS Tangerang include the low percentage of students who graduate on time compared to students who graduate not on time. In the 2015/2016 graduation year, only 30% of students passed on time, the rest did not graduate on time. This study aims to assist the Faculty of Engineering in predicting student graduation, so that it can be anticipated earlier. This research uses the attributes of total credits, 1st semester IP, 2nd semester IP, 3rd semester IP, 4th semester IP. The methods used in this research are Naïve Bayes and Artificial Neural Network. The data used in this study used 330 records of students who graduated in 2012-2016. The results of the accuracy obtained after testing with the system using 20% data testing obtained an accuracy of 63.63%, 71.05% precision, 67.5% recall, and 62.6% AUC. LPPM Achmad Solichin, Budi Luhur University, Department of Information System 2022-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11816 10.30998/faktorexacta.v15i1.11816 Faktor Exacta; Vol 15, No 1 (2022) Faktor Exacta; Vol 15, No 1 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11816/4789 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15450 2023-03-25T15:11:52Z Faktor_Exacta:ART

Development of a Production Machine Maintenance Predictive Model Using the Elman Recurrent Neural Network Algorithm Zatmika, Ajat; Universitas Krisnadwipayana Kartika, Harry Dwiyana; Universitas Krisnadwipayana Khumaidi, Ali; Universitas Krisnadwipayana PT Simba Indosnack Makmur is a factory that produces snacks. In the production process the machine has worked very optimally, the problem that is often faced by the Quality Control department is often finding non-standard product weights. This problem is caused by a machine that already requires maintenance. So far, the maintenance process has to get approval from the manager, which sometimes takes quite a long time to be inspected so that the maintenance process is delayed, which results in reduced production targets. By implementing a predictive maintenance model that utilizes time series data in the production process, applying the Elman Recurrent Neural Network will be able to provide notifications for machine maintenance before the machine is inaccurate in snack production. The Elman structure was chosen because it can make iterations much faster, thus facilitating the convergence process. The input vector used uses windows size. The results of the study using a target error of 0.001 show the smallest MSE value of 0.002833 with windows size 11. Then by using 13 neurons in the hidden layer a minimum error value of 0.003725 is obtained. LPPM 2023-03-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15450 10.30998/faktorexacta.v16i1.15450 Faktor Exacta; Vol 16, No 1 (2023) Faktor Exacta; Vol 16, No 1 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i1 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15450/5528 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/22013 2024-05-03T08:18:17Z Faktor_Exacta:ART

Evaluasi Kinerja Prophet untuk Prediksi Harga Emas Berjangka Primawati, Alusyanti; (Scopus ID: 57205058978) Universitas Indraprasta PGRI Trinoto, Andreas Adi; Universitas Indraprasta PGRI Penelitian ini bertujuan untuk mengevaluasi kinerja model prediksi Prophet dalam konteks prediksi harga emas dengan menggunakan dua metrik evaluasi, Mean Absolute Percentage Error /MAPE dan uji koefisien determinasi / R-squared. Data historis harga emas yang digunakan untuk melatih dan menguji berasal dari Kaggle.com dalam kurun waktu 12 tahun, MAPE digunakan untuk mengukur tingkat kesalahan relatif dalam prediksi harga emas berjangka, sementara R-squared digunakan untuk menilai sejauh mana model Prophet dapat menjelaskan variasi dalam data harga emas berjangka. Dari 6 skenario hasil evaluasi model, skenario 2 dan 5 mendapat hasil terbaik yaitu skenario 2 memiliki nilai R2 = 0.19 dan MAPE = 3.31%. Skenario 5 memiliki MAPE = 2.13% dan R2 =0.07. Prophet dapat menjelaskan prediksi perubahan harga emas berjangka dengan akurat, hasil ini memiliki implikasi penting dalam pengambilan keputusan investasi dan perdagangan di pasar emas berjangka. LPPM 2024-05-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22013 10.30998/faktorexacta.v17i1.22013 Faktor Exacta; Vol 17, No 1 (2024) Faktor Exacta; Vol 17, No 1 (2024) 2502-339X 1979-276X 10.30998/faktorexacta.v17i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22013/6606 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/7715 2024-01-03T13:54:02Z Faktor_Exacta:ART

Pendekatan HOT-Fit dalam Evaluasi Sistem Informasi Manajemen Penyelesaian Laporan (SIMPeL) pada Lembaga Ombudsman Republik Indonesia Andriyani, Meilianti; Universitas Gunadarma Umniati, Naeli; Gunadarma University SIMPeL is a system owned by ORI to facilitate the process of completing reports and improving public services. The increasing number of incoming public service maladministration reports is a challenge for ORI, especially how to monitor the number of incoming reports quickly and make the handling of reporting (complaints) integrated. This research uses the HOT-FIT analysis method to determine influence of Human components, Organization, and Technology influences on the utilization (Net Benefit) of SIMPeL in ORI office. The number of respondents in this study was 79 respondents SIMPeL users who are staff or assistants at representative offices and ORI headquarters. The data in this study were analyzed with the help of the SmartPLS program. The results of the study show that simultaneously humans, organizations, and technology are components that affect the utilization (Net Benefit) of SIMPeL in ORI. The effect of human, organizational, and technological components on SIMPeL utilization was 37.8% while the remaining 62.2% variance in SIMPeL utilization was influenced by other factors outside the three components. Partially, human and technology can also affect the utilization (Net Benefit) of SIMPeL in ORI, while organizational components are not an influential factor in the utilization of SIMPeL in ORI. LPPM 2021-02-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7715 10.30998/faktorexacta.v13i4.7715 Faktor Exacta; Vol 13, No 4 (2020); 243-254 Faktor Exacta; Vol 13, No 4 (2020); 243-254 2502-339X 1979-276X 10.30998/faktorexacta.v13i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7715/3718 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/7715/1283 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/23322 2024-10-30T05:13:35Z Faktor_Exacta:ART

Implementasi Algoritma Greedy Untuk Optimalisasi Jarak Tempuh Rute Ziarah Makam Walisongo Bachtiar, Yogi Religious tourism is one of the acts of visiting a place carried out by religious people. Tourist trips sometimes face problems because some places are far apart. One important element of the pilgrimage process that must be carefully considered is the mileage of the pilgrimage route. Choosing a route that matches the distance and travel time allows many advantages, one of which is the reduction of fuel consumption during the trip. this research will try to use the Greedy Algorithm to solve the problem of optimizing the Walisongo pilgrimage tour route on the island of Java. As a result, the selected route on the Walisongo Tomb pilgrimage is S?W9?W8?W7?W6?W3?W5? W1?W1?W2 with a total travel distance of 819 Km (one way). The use of the Greedy algorithm does not always produce the shortest route, but will result in the selection of the Optimum route from one location to the next. So that it can save fuel usage and travel time between locations. LPPM 2024-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23322 10.30998/faktorexacta.v17i3.23322 Faktor Exacta; Vol 17, No 3 (2024); 287-294 Faktor Exacta; Vol 17, No 3 (2024); 287-294 2502-339X 1979-276X 10.30998/faktorexacta.v17i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23322/7104 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10376 2022-01-26T13:47:30Z Faktor_Exacta:ART

Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang Suradiradja, Kahfi Heryandi Chilli consumption keeps increasing along with the annual population increase in Indonesia. Meanwhile, chilli prices also fluctuate due to rainfall, affecting production and inflation. In the industrial era 4.0, IT support is crucial in various fields including in agriculture such as chilli planting to help stakeholders, both in the economy and agriculture sectors, make decisions based on accurate predictive data support. The study aims to compare the accuracy of two machine learning algorithm models, i.e., Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN), for time-series regression implementable to predict chilli prices in Tangerang City. The experimental method stages include business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The required dataset attributes include red chilli prices, date, inflation, and rainfall. This research is expected to contribute to machine learning algorithms to assist stakeholders and to be implemented by information system developers. The research result indicates that the MLP algorithm with the rmsprop optimizer performs better than the RNN with the metric measurement of Loss = 0.0038, MSE = 10271959,0 and MAPE = 3.79%. Suggestions for further research include the urgency to innovate architectural models, either for activation functions, optimizers, or other regression algorithms for better metric measurement results. LPPM 2022-01-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10376 10.30998/faktorexacta.v14i4.10376 Faktor Exacta; Vol 14, No 4 (2021); 194-205 Faktor Exacta; Vol 14, No 4 (2021); 194-205 2502-339X 1979-276X 10.30998/faktorexacta.v14i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10376/4502 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/10376/1852 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/10376/1853 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/10376/1854 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/11928 2023-01-24T13:45:21Z Faktor_Exacta:ART

Sistem Pendukung Keputusan Pemilihan Guru Terbaik Menggunakan Profile Matching Bachtiar, Yogi Teachers are a big foundation in education in Indonesia, without teachers we all will not be smart. There is a need for great respect for teachers, both from the government and schools. There is a need for great respect for teachers, both from the government and schools. teaching teachers must pay attention and give appreciation to teachers so that teachers become enthusiastic in teaching activities. Many teachers have achievements. In giving assessment criteria, subjective ones are often used with good, sufficient and insufficient comparisons so that the teacher's assessment is deemed inappropriate or objective with reality. used to assist decision making. In this paper, a decision support system was chosen using the profile matching method. It is hoped that the teacher selection process can be carried out quickly and accurately) LPPM 2023-01-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11928 10.30998/faktorexacta.v15i4.11928 Faktor Exacta; Vol 15, No 4 (2022); 283-289 Faktor Exacta; Vol 15, No 4 (2022); 283-289 2502-339X 1979-276X 10.30998/faktorexacta.v15i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11928/5358 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/19767 2024-01-09T11:00:07Z Faktor_Exacta:ART

Perancangan Diagnosa Covid-19 Menggunakan Metode Case Based Reasoning (CBR) Untuk Mengidentifikasi Tingkatan Gejala Pasien Covid-19 Rismawati, Nofita; Universitas Indraprasta PGRI Mulya, Muhamad Femy; Universitas Tanri Abeng COVID-19 is a disease caused by a new coronavirus derivative, 'CO' taken from coronavirus, 'VI' virus, and 'D' disease (disease). The outbreak of this disease has shaken the world community so much that almost 200 countries in the world have been infected by this virus, including Indonesia. To reduce the number of COVID-19 sufferers, an expert system is needed that can carry out the process of identifying the symptoms of COVID-19 so that it is easier for patients to identify the type of COVID-19 they are suffering from and not make mistakes in the treatment process. Based on this background, researchers are interested in designing an expert system application that can assist the COVID-19 diagnosis process and can be used for public services such as health centers, clinics, and hospitals. The method used by researchers in designing this expert system is the case-based reasoning method. CBR is an approach to solving a problem based on solutions to previous problems. CBR has four process stages: retrieve, reuse, revise, and retain. From the results of testing six important features in the application involving five application users using the Blackbox Testing method, a testing percentage of 98.9% was obtained, which means that the application test results are in accordance with the user's wishes, and this application is expected to help in identifying patients with COVID-19 symptoms.Keywords: Covid-19, Case-Based Reasoning (CBR), Blackbox Testing LPPM 2024-01-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19767 10.30998/faktorexacta.v16i4.19767 Faktor Exacta; Vol 16, No 4 (2023) Faktor Exacta; Vol 16, No 4 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19767/6289 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/6426 2020-12-17T16:05:07Z Faktor_Exacta:ART

Orkestrasi Cloud Dengan Chef, Menuju Keselarasan Sistem Otomasi Teknologi Informasi Bramasto, Suryo; Program Studi Informatika, Institut Teknologi Indonesia Indriasari, Melani; Program Studi Informatika Institut Teknologi Indonesia D., Endang Ratnawati; Program Studi Informatika Institut Teknologi Indonesia Abstrak. Penelitian pada artikel ini mengimplementasikan orkestrasi dari otomasi layanan-layanan teknologi informasi berbasis cloud, yang mana dari sisi pengguna merupakan solusi-solusi bisnis. Orkestrasi dilakukan dengan salah satu mesin orkestrasi yakni Chef. Layanan-layanan teknologi informasi yang otomasi pembangunan dan pengelolaannya pada cloud diorkestrasi dengan Chef dibangun dengan web server Apache, database server MySQL, dan load balancer Nginx. Orkestrasi dilakukan dengan membangun arsitektur mesin orkestrasi spesifik yakni Chef pada layanan cloud AWS (Amazon Web Services), kemudian membuat recipes dan cookbooks pada Chef yang berisi perintah-perintah otomasi dan orkestrasi layanan-layanan teknologi informasi. Orkestrasi memungkinkan sebagian besar pekerjaan dari system administrator untuk dilakukan secara otomatis bahkan pada layanan-layanan IT berbasis Cloud yang kompleks, dengan tetap memungkinkan untuk dilakukan pengelolaan layanan sebagaimana yang biasa dilakukan oleh system administrator pada sub-sub sistem dari layanan-layanan IT. Selain itu orkestrasi pada implementasinya akan sangat mendukung pendekatan DevOps pada rancang bangun piranti lunak sebagai sub sistem utama dari layanan IT. Kata Kunci: Chef, cloud, DevOps, orkestrasi, otomasi LPPM 2020-11-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6426 10.30998/faktorexacta.v13i3.6426 Faktor Exacta; Vol 13, No 3 (2020) Faktor Exacta; Vol 13, No 3 (2020) 2502-339X 1979-276X 10.30998/faktorexacta.v13i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6426/3533 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10591 2021-12-24T14:22:10Z Faktor_Exacta:ART

Prediksi Analisis Penderita Covid19 di Indonesia dengan Metode Linier Regresi dan Unsupervised Learning Cahyana, Yana; Universitas Buana Perjuangan Karawang Siregar, Amril Mutoi Penyakit Covid-19 sekarang ini telah dinyatakan penyeakit pandemic, karena tingkat penyebaran dan resiko yang ditimbulkan sangat berbahaya. Berbagai langkah seperti program awareness, social distancing, dan contact tracing telah dilakukan untuk mengendalikan wabah COVID-19. Jika tidak ada vaksin, prediksi kasus yang dikonfirmasi, meninggal, dan pulih diperlukan untuk meningkatkan kapasitas sistem perawatan kesehatan dan mengendalikan penularan. Dalam studi ini, kasus kumulatif dan harian dikonfirmasi, meninggal, dan pulih di Indonesia. Analisisa tidak mempertimbangkan perubahan apa pun dalam tindakan pengendalian pemerintah. Informasi dari studi ini dapat memberikan informasi yang relevan kepada pemerintah dan pejabat Kesehatan dan masyarakat. Bagaimana tingkat kesembuhan terhadap terkonfirmasi, tingkat kematian terhadap jumlah penderita. Penelitian ini menggunakan model regresi dan clustering dengan K-means, menggunakan unsupervised learning dan supervised learning untuk membangun distribusi model. Hasil penelitian ini dengan metode regresi dengan R2 = 0.99 sedangkan untuk clustering denga K= interval 10 - 15 dilihat dari hasil metode elbow LPPM Universitas Buana Perjuangan Karawang 2021-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10591 10.30998/faktorexacta.v14i3.10591 Faktor Exacta; Vol 14, No 3 (2021); 107-116 Faktor Exacta; Vol 14, No 3 (2021); 107-116 2502-339X 1979-276X 10.30998/faktorexacta.v14i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10591/4324 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/12820 2022-11-28T13:59:40Z Faktor_Exacta:ART

Pengembangan Model RNN untuk Prediksi Produksi Daging Sapi dalam Perencanaan Pembangunan Nasional Yulianingsih, Yulianingsih; Universitas Indraprasta PGRI Akhirina, Tri Yani; Universitas Indraprasta PGRI Niswati, Za’imatun; Universitas Indraprasta PGRI Data is an important component because it will support in policies / decisions making, serve as control tools to prevent error from occuring and support transparent, accountable and participative governance. This study examines the prediction of beef production and product consumption with the Long Short Term Memory (RNN LSTM) Recurrent Neural Network approach. Using statistical data on beef production and consumption of products per capita per week from BPS. The data used were 12 records for each data source. LSTM contains information outside the normal flow of recurrent network in the gate cell. Cell makes decisions about what should be stored and when to permit reading, writing and deletion, through open and closed gates. The gate conveys information based on the strength that enters into it and will be filtered to be the weight of the gate itself. These weights are the same as the input and hidden unit weights that are adjusted through learning process on the recurrent network. The results of research carried out by building prediction models of beef production and product consumption get the best results using data for 3 years with RMSE 32121.297 for beef production and 0.001 for product consumption. LPPM 2022-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12820 10.30998/faktorexacta.v15i3.12820 Faktor Exacta; Vol 15, No 3 (2022); 200-205 Faktor Exacta; Vol 15, No 3 (2022); 200-205 2502-339X 1979-276X 10.30998/faktorexacta.v15i3 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12820/5143 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/17685 2023-11-10T10:42:33Z Faktor_Exacta:ART

Sistem Pakar Rekomendasi Pendakian Gunung di Jawa Tengah menggunakan Algoritma Fuzzy Tsukamoto Berbasis Website Anggun Pratiwi, Cut Tesya Iftillah Norhikmah, Norhikmah Indonesia has a variety of unique tours both in terms of nature and history that can be used as tourist attractions for local and foreign people, with a variety of beauties that can be enjoyed. One of them is the mountainous area which presents views that can soothe and spoil the eyes in almost every area of Indonesia which has different characteristics of mountains, especially the island of Java, to be precise in the area of Central Java, has about 12 mountains which are usually used as climbing places for residents in or outside the region, in addition to helping the people's economy, climbing can also be used to protect nature and can introduce the beauty of nature in Indonesia to the international community. Therefore a system was created to recommend mountain climbing in the Central Java area based on a website using the Fuzzy Tsukamato method LPPM Universitas AMIKOM Yogyakarta 2023-10-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17685 10.30998/faktorexacta.v16i3.17685 Faktor Exacta; Vol 16, No 3 (2023) Faktor Exacta; Vol 16, No 3 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17685/6116 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/17685/3531 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/22442 2024-07-17T04:29:56Z Faktor_Exacta:ART

Analisis Sentimen Terhadap Kontroversi Putusan MK Mengenai Usia Capres-Cawapres Menggunakan Multi-Layer Perceptron Dengan Teknik SMOTE Sasmita, Sasmita Jariah S.Intam, Rezki Nurul; Universitas Negeri Makassar Surianto, Dewi Fatmarani; Universitas Negeri Makassar B, Muhammad Fajar; Universitas Negeri Makassar In October 2023, the Constitutional Court's decision on age limit requirements for presidential and vice-presidential candidates stirred controversy, perceived as favoring a specific vice-presidential candidate. Public reactions flooded social media platforms, particularly on Najwa Shihab's YouTube channel, where sentiment analysis was conducted on 505 comments under the video titled "Putusan MK: Publik memang Seharusnya Marah" (Constitutional Court Decision: The Public Should Indeed Be Angry). The comments were categorized into three sentiment classes: 425 negative, 42 neutral, and 38 positive. The study employed Multi-Layer Perceptron (MLP) models tested on both imbalanced and balanced data using the SMOTE oversampling technique. Two feature extraction methods, TF-IDF weighting and countvectorizer, were applied. Results showed that the combination of TF-IDF with balanced data yielded the most effective classification model, boasting a remarkable accuracy, precision, recall, and F1-score, each at 99%. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22442 10.30998/faktorexacta.v17i2.22442 Faktor Exacta; Vol 17, No 2 (2024); 188-198 Faktor Exacta; Vol 17, No 2 (2024); 188-198 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22442/6800 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/8734 2021-04-27T14:57:10Z Faktor_Exacta:ART

ANALISIS KLASIFIKASI POPULASI TERNAK KAMBING DAN DOMBA DENGAN MODEL CONVOLUTIONAL NEURAL NETWORK Primawati, Alusyanti; University of Indraprasta PGRI Mutia, Intan; University of Indraprasta PGRI Marlina, Dwi; University of Indraprasta PGRI The number of goat populations is increasing all over the world. Sheep and goats are economically potential for business development because they do not require large areas of land, relatively small investment in business capital, and are easy to market. However, the similarities between goats and sheep can make small breeders who are just starting out in business nervous. Therefore, in goats and sheep, an intensive and efficient Precision Livestock Farming system is required. To answer this problem, goat and sheep objects was studied out using the collaboration software programming R and Python which executed in RStudio editor and Anaconda3 with the Tensor flow package. The sample data of 40 images. The model obtained from the classification results uses 20 pictures of goats and 20 pictures of sheep for training and testing. The accuracy produced shows that the prediction of training data at epoch 70 and 100 has the right accuracy with the actual data. This reinforces that the model used is good (fit) to the training dataset, but when it is applied to the testing dataset, the prediction results are still close to perfect. Epoch 70 identifies there is 1 image of a Goat which is recognized as Lamb. LPPM 2021-03-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8734 10.30998/faktorexacta.v14i1.8734 Faktor Exacta; Vol 14, No 1 (2021); 22-33 Faktor Exacta; Vol 14, No 1 (2021); 22-33 2502-339X 1979-276X 10.30998/faktorexacta.v14i1 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8734/3945 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/8734/1481 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/12106 2022-08-10T14:48:29Z Faktor_Exacta:ART

Algorithm Analysis of K-Means and Fuzzy C-Means for Clustering Countries Based on Economy and Health Wulandari, Lily; Gunadarma University Yogantara, Bima Olga; Gunadarma University Clustering adalah teknik pembelajaran mesin tanpa pengawasan yang membagi populasi menjadi beberapa kelompok atau klaster sedemikian rupa sehingga data dalam kelompok yang sama mirip satu sama lain, dan data dalam kelompok yang berbeda tidak serupa. Algoritma clustering yang ada diantaranya algoritma K-Means dan Fuzzy C-Means. Pada makalah ini proses clustering dilakukan untuk mengelompokkan negara-negara di dunia menjadi dua kategori utama yaitu negara maju dan negara berkembang berdasarkan tingkat kesejahteraan masyarakatnya. Makalah ini membahas tentang perbandingan algoritma K-Means dan Fuzzy C-Means. Algoritma K-Means menghasilkan 32 negara maju dan 135 negara berkembang. Algoritma Fuzzy C-Means menghasilkan 33 negara maju dan 134 negara berkembang. Hasil analisis pengujian performa menggunakan parameter Davies Bouldin Index pada algoritma K-Means memiliki nilai paling kecil artinya lebih baik yaitu sebesar 0.6606398 DB. Sedangkan hasil pengujian parameter Silhouette Coefficient pada Fuzzy C-Means semakin besar nilainya semakin baik dan didapatkan nilainya sebesar 0.896 S. Pengujian yang cukup signifikan terlihat pada penilitian ini adalah hasil pengukuran parameter Execution Time pada algoritma K-Means sebesar 0.00199 detik dan jauh lebih cepat. LPPM Gunadarma University 2022-08-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12106 10.30998/faktorexacta.v15i2.12106 Faktor Exacta; Vol 15, No 2 (2022) Faktor Exacta; Vol 15, No 2 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i2 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12106/4917 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/12106/2239 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/16512 2023-07-31T10:48:07Z Faktor_Exacta:ART

Rancang Bangun Sistem Antrian Pintar Klinik Gigi Menggunakan Raspberry Pi Gunawan, Ardi; Universitas Pelita Bangsa Riady, Sasmitoh Rahmad; Universitas Bina Insani Nawangsih, Ismasari; Universitas Pelita Bangsa Kinasih, Rianti; Universitas Pelita Bangsa Large-Scale Social Restrictions were imposed in Indonesia in 2020 as a response to the 2019 coronavirus disease (Covid-19), which has become a pandemic, including in Indonesia. The government's appeal regarding social distancing has made many dental clinics move to provide excellent service while still paying attention to social distancing policies. implementation of new policies and ways to overcome challenges by minimizing physical contact while still running business optimally and meeting patient needs. Before the pandemic occurred, patients were required to take a queue number first at the service location and then wait for the number to be called. Under the current conditions, in the midst of the COVID-19 pandemic, you must avoid crowds and maintain physical distance when interacting socially. In the description of the problem, we propose a smart queue as a solution to avoid crowds when going to the dentist's office. Smart Queuing System based on IoT with a Raspberry Pi camera capable of scanning QR codes as validation and a Raspberry Pi serving as a queue validation data server. This system will be used for online queues at dentist clinics. An online registration system determines whether registration is open or closed at the time of registration. With this system, it is hoped that online registration will be more efficient and orderly. LPPM 2023-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/16512 10.30998/faktorexacta.v16i2.16512 Faktor Exacta; Vol 16, No 2 (2023) Faktor Exacta; Vol 16, No 2 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/16512/5824 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/19405 2024-07-17T04:29:56Z Faktor_Exacta:ART

Electronic Voting (e-voting) sebagai Aplikasi Terdesentralisasi pada Vexanium Blockchain Bramasto, Suryo; Program Studi Informatika, Institut Teknologi Indonesia Savitri, Sandriana Febia; Institut Teknologi Indonesia Djuwitaningrum, Endang Ratnawati; Institut Teknologi Indonesia Decentralized applications, or dApps, are software programs that run on a blockchain or peer-to-peer (P2P) network of computers instead of on a single computer. DApps (also called "dapps") are thus outside the purview and control of a single authority. On this research, a DApps for electronic voting (e-voting) that run on Vexanium blockchain is built. Besides Vexanium platform and toolchain, PHP 8.2, Apache web server, and PostgreSQL also used for building e-voting DApps in this research. A specific method for building application on Vexanium blockchain was also implemented. The contract implemented in this research is limited on smart contract, even though actually Ricardian contract is the default template on Vexanium blockchain. E-voting DApps on Vexanium blockchain on this research has successfully developed and has gone through the black box method validation testing process. For the next step of the development, Ricardian contract should be implemented and also the security at blockchain level should also be implemented such as building reentrancy attacks mechanism, improving the source of randomness for the nonce, ad safeguards against frontrunning. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19405 10.30998/faktorexacta.v17i2.19405 Faktor Exacta; Vol 17, No 2 (2024); 96-107 Faktor Exacta; Vol 17, No 2 (2024); 96-107 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19405/6791 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/24751 2025-02-25T08:18:00Z Faktor_Exacta:ART

Optimalisasi Model Klasifikasi Naive Bayes dan Support Vector Machine Dengan Fast Text dan Chi Square Pada Analisis Sentimen Penyelenggaraan Pembelajaran Pemrograman di Fasilkom Universitas Mercu Buana Fajriah, Riri; Universitas Budi Luhur Kurniawan, Denni; Universitas Budi Luhur The implementation of effective programming learning at the Faculty of Computer Science, Universitas Mercu Buana is one of important strategy. This expectation is constrained because the results of the evaluation of the competency achievements of many graduates have not mastered programming skills well. Therefore, the research conducted is related to analyzing the sentiments of all stakeholders who have been involved with the implementation of programming learning. The data source based on the results of an online questionnaire. The sentiment data analysis process uses the Cross Industry Standard Process for Data Mining method with the Naive Bayes and Support Vector Machine classification models. The result of the research is an increase in the accuracy of sentiment analysis data processing which previously only used the Naive Bayes Algorithm only achieving an accuracy of 65.56% and by optimizing with Feature Extraction Fast Text, the accuracy achievement increased to 90.49%. While optimizing the algorithm using Feature Selection Chi Square can make the Support Vector Machine classification model optimized to achieve an accuracy value of 99.58% from the previous accuracy achievement was 90.72%. This research can prove that optimizing the application classification model algorithms can use using Fast Text and Chi Square techniques. LPPM 2025-02-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/24751 10.30998/faktorexacta.v17i4.24751 Faktor Exacta; Vol 17, No 4 (2024); 334-345 Faktor Exacta; Vol 17, No 4 (2024); 334-345 2502-339X 1979-276X 10.30998/faktorexacta.v17i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/24751/7386 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/24751/5339 Copyright (c) 2025 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10456 2022-07-14T12:04:43Z Faktor_Exacta:ART

PENERAPAN METODE EARNED VALUE ANALYSIS MENGGUNAKAN SOFTWARE PRIMAVERA PROJECT PLANNER PADA PEMBANGUNAN INSTALASI PENGOLAHAN AIR LIMBAH Agnessia, Nadila; Program Studi Teknik Industri, Fakultas Teknik dan Ilmu Komputer, Universitas Indraprasta PGRI, Jl. Nangka Raya No. 58 C, Tanjung Barat, Jagakarsa, Jakarta Selatan Indrajaya, Drajat; Universitas Indraprasta PGRI PT Gasd Geosby Indonesia is a company engaged in environmental services and construction. PT Gasd Geosby Indonesia is handling a Wastewater Treatment Plant (IPAL) construction project in Cikarang for an industrial company. The project experienced delays due to several factors supporting activities. Factors that can affect delays include people, materials, costs, and the tools used. Delay cannot be avoided but can be controlled. One form of project control is to calculate the project completion time and the amount of costs that will be incurred until the project is completed. One of the methods used to estimate the time and cost of the project according to the budget according to the work that has been completed is Earned Value Analysis (EVA). The result of data processing using the Primavera application is Estimate At Completion (EAC) in the 4th week of Rp. 185.682.084, the estimated cost is greater than the project planning cost of Rp. Rp. 147.794.994 if the trend of project implementation during the visit did not change until the end of implementation. From the data obtained based on the results of the field visit at week 4, the estimated completion of the project for 43 days or 14 days from the last day of visit was obtained. LPPM 2022-05-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10456 10.30998/faktorexacta.v15i1.10456 Faktor Exacta; Vol 15, No 1 (2022) Faktor Exacta; Vol 15, No 1 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i1 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10456/4787 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15138 2023-03-25T15:10:56Z Faktor_Exacta:ART

IDENTIFIKASI GARIS TELAPAK TANGAN DENGAN METODE MOBILENET CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM PRESENSI SISWA Sukriyandi, Muhammad Hamdi Solichin, Achmad; Universitas Budi Luhur The attendance system at SMK Taruna Terpadu 1 with nine majors is still done manually. With a total of about 5,000 students, If attendance is recorded manually, many of these statistics are cumulative, making them difficult to organize and find when needed. Digitization of attendance recording is expected, one of which is the biometric method. Biometrics, the technology that digitally recognizes organic characteristics, can potentially update maps and other identifiers. Biometrics themselves come in physical form, such as faces, irises, fingerprints, and handprints. However, at some point during the COVID-19 pandemic, contact fingerprinting is unavailable and many of the challenges facing facial recognition, starting with skin color, using mask and identical twins. suggest ways to avoid contact. Fingerprint biometrics are an attractive option for more accurate, reliable, and secure contactless human identification technology, but identifying palm features from past images is also an attractive option. I am tasked with inputting some of the palm functions. and lighting fixtures. In this article, the authors propose to apply MobileNeV2's use of augmented facts, ROI detection, and pre-trained convolutional neural community (CNN) models. After testing with the dataset that the author got from SMK Taruna Terpadu 1 by performing data augmentation, ROI detection and identification with the pretrained MobileNetV2 model, it turns out to get the best accuracy results up to 99.98%. LPPM 2023-03-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15138 10.30998/faktorexacta.v16i1.15138 Faktor Exacta; Vol 16, No 1 (2023) Faktor Exacta; Vol 16, No 1 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15138/5524 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/15138/3000 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/19593 2024-05-03T08:20:25Z Faktor_Exacta:ART

Analisis dan Optimasi Sistem Kendali Robot Falcon Millenium: Automatic Robot Palletizer Menggunakan PLC Omron Wijaya, Ahmad Reynaldi Mandasari, Raden Deasy; Universitas Bina Sarana Informatika Rosano, Andi; Universitas Bina Sarana Informatika The main objective of this research is to analyze and optimize the control system of this robot through simulations to improve efficiency and production quality in the ceramic industry or other manufacturing sectors. The study consists of several stages, including a literature review to comprehend the concepts of the Falcon Millennium Robot, PLC Omron, and relevant theories in automation and robotics. Subsequently, problem identification is conducted to pinpoint areas that require analysis and optimization. The next step involves designing a Ladder Diagram using CX-Programmer software to control the automatic palletization process. Additionally, the control system's user interface (HMI) is designed using CX-Designer to facilitate the operator in controlling the system. Once the design is completed, simulations are conducted to verify the robot's performance and the palletization process before physical implementation. Experimental data is analyzed and collected in tables to measure efficiency, Conveyor 1 and Conveyor 2 speeds, and the time taken to transfer boxes to the warehouse. The analysis results show an average efficiency of approximately 88.5% in the automatic palletization process. Conveyor 1 and Conveyor 2 speeds increase gradually, and the average time taken to transfer boxes to the warehouse is around 41 seconds. LPPM 2024-05-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19593 10.30998/faktorexacta.v17i1.19593 Faktor Exacta; Vol 17, No 1 (2024) Faktor Exacta; Vol 17, No 1 (2024) 2502-339X 1979-276X 10.30998/faktorexacta.v17i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19593/6609 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/7074 2021-02-18T00:15:09Z Faktor_Exacta:ART

Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning Saputra, Irwansyah AJI PAMBUDI, RAHMAD SINGGIH; STMIK Nusa Mandiri DARONO, HANAFI EKO; STMIK Nusa Mandiri AMSURY, FACHRI; STMIK Nusa Mandiri FAHDIA, MUHAMMAD RIZKI; STMIK Nusa Mandiri RAMADHAN, BENNI; STMIK Nusa Mandiri ARDIANSYAH, ANGGIE; STMIK Nusa Mandiri A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall. LPPM 2021-02-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7074 10.30998/faktorexacta.v13i4.7074 Faktor Exacta; Vol 13, No 4 (2020); 200-207 Faktor Exacta; Vol 13, No 4 (2020); 200-207 2502-339X 1979-276X 10.30998/faktorexacta.v13i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7074/3723 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/10010 2021-12-24T14:22:10Z Faktor_Exacta:ART

Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear Niswati, Za'imatun; (Scopus ID:57205060572) Universitas Indraprasta PGRI Hardatin, Rahayuning; IPB University Muslimah, Meia Noer; IPB University Hasanah, Siti Nur; IPB University Cervical cancer is one of the most deadly types of cancer in women. According to Ferlay et al. (2018) cervical cancer ranks second for the type of cancer that attacks women the most. Data from the Indonesian Ministry of Health, there are at least 15000 cases of cervical cancer every year in Indonesia. This cancer is a type of tumor that develops in the epithelial tissue of the cervix. In addition to HPV vaccination, cervical cancer detection can also be carried out with a Pap smear test and VIA examination supported by medical image tests such as CT scan, microscopic and MRI (Akbar et al. 2021). Pap smear test is a type of test to detect cervical cancer which is quite widely used because the cost of the test is cheaper than the HPV vaccination. This test is carried out by taking samples of uterine cells which are then analyzed for early detection of cervical cancer (BPJS Kesehatan 2020). Through a pap smear can be found the presence of HPV infection and abnormal cells that can turn into cancer cells. The purpose of this research is to apply the ResNet50 and ResNet101 architectures on pap smear images to identify cervical cancer and evaluate the performance of the ResNet50 and ResNet101 architectures in the classification of cervical cancer on pap smear images. In this study, CNN ResNet50 and ResNet101 were used to classify cervical cancer on pap smear images. This study has created two models to predict the grade of cervical cancer on pap smear images. The ResNet50 architecture gets 91% accuracy while the ResNet101 architecture gets 89%. Although the architecture of ResNet101 is more complex than ResNet50, but if viewed from the results of the model evaluation, ResNet101 has a worse performance. This is due to the relatively small training data when trained with a large architecture such as ResNet101, not necessarily resulting in better accuracy. LPPM 2021-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10010 10.30998/faktorexacta.v14i3.10010 Faktor Exacta; Vol 14, No 3 (2021); 160-167 Faktor Exacta; Vol 14, No 3 (2021); 160-167 2502-339X 1979-276X 10.30998/faktorexacta.v14i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/10010/4325 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/11419 2023-01-24T12:06:17Z Faktor_Exacta:ART

Pembuatan Peta 3D Urban Model Untuk Visualisasi Dampak Banjir Mabrur, Adkha Yulianandha; Prodi Teknik Geodesi Fakultas Teknik Sipil dan Perencanaan (FTSP) Institut Teknologi Nasional Malang Arafah, Feny; Prodi Teknik Geodesi Fakultas Teknik Sipil dan Perencanaan (FTSP) Institut Teknologi Nasional Malang Agustina, Fransisca Dwi; Prodi Teknik Geodesi Fakultas Teknik Sipil dan Perencanaan (FTSP) Institut Teknologi Nasional Malang Suganda, Lalu Teguh; Prodi Teknik Geodesi Fakultas Teknik Sipil dan Perencanaan (FTSP) Institut Teknologi Nasional Malang 3D modeling is a process to create 3D objects that you want to put in a visual form. A 3D model is a mathematical representation of any three-dimensional object (either inanimate or living). A model is technically graphical until it is visually displayed. Because 3D models are not limited to virtual space. A model can be displayed visually as a two-dimensional image through a process called 3D rendering, or used in non-graphical computer simulations and calculations. In this case, the geographic information system can present a form of modeling of a hydrological phenomenon such as flooding in an area. This study aims to analyze the flood and visualize it in the form of three-dimensional modeling to see the impact of a flood threat due to the Jelateng river’s overflow. This study emphasizes information related to the impact caused by the overflow of the Jelateng river. Making a 3D urban map model will be used as a representation of the appearance of the Jelateng river area and then it will be visualized using DEMNAS data on the arcscene with the animation manager so that the visualization can be seen according to the scenario that will be carried out. The results of the research will be published in a journal so that it can be a reference for some users who want to know related information from the research results LPPM Institut Teknologi Nasional Malang 2023-01-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11419 10.30998/faktorexacta.v15i4.11419 Faktor Exacta; Vol 15, No 4 (2022); 243-251 Faktor Exacta; Vol 15, No 4 (2022); 243-251 2502-339X 1979-276X 10.30998/faktorexacta.v15i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11419/5352 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/11419/3029 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/19216 2024-01-09T10:59:29Z Faktor_Exacta:ART

Deteksi Pelat Nomor Dengan Menggunakan Optical Character Recognition Berbasis Algoritma LSTM Wardhani, Elisa; Universitas Mercu Buana Dwiasnati, Saruni; Universitas Mercu Buana Illegal Parking which has become a frequent occurrence in the Jakarta area, encourages system transformation which includes the addition of automatic parking system facilities based on the building infrastructure and facilities regulation. The development of an automatic parking system can utilize license plate number detection to minimize the need to manually input license plate numbers into the parking system. In this study, LSTM algorithm training is done and implemented on Optical Character Recognition to detect license plate numbers accurately. Based on the evaluation results, the LSTM algorithm has a good performance in detecting license plate numbers with an accuracy rate of 86,36%. However, the LSTM algorithm performance improved when implemented on Optical Character Recognition with an accuracy rate of 95,8%. Hence, based on the evaluation, the LSTM algorithm that has been implemented on Optical Character Recognition is considered a preferable choice in license plate number detection as it has a higher level of accuracy compared to the use of the LSTM algorithm alone. LPPM 2024-01-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19216 10.30998/faktorexacta.v16i4.19216 Faktor Exacta; Vol 16, No 4 (2023) Faktor Exacta; Vol 16, No 4 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/19216/6287 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/19216/3883 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/6588 2023-02-22T08:04:29Z Faktor_Exacta:ART

Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang) Rivanthio, Tubagus Riko; Politeknik LP3I Bandung Ramdhani, Mardhiya; Politeknik LP3I Bandung Sahi, Ahmad; Politeknik LP3I Bandung SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.Key words: clustering, dataMining, suitability, majors, students LPPM 2020-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6588 10.30998/faktorexacta.v13i2.6588 Faktor Exacta; Vol 13, No 2 (2020); 125-131 Faktor Exacta; Vol 13, No 2 (2020); 125-131 2502-339X 1979-276X 10.30998/faktorexacta.v13i2 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6588/3308 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/21920 2024-10-30T05:13:35Z Faktor_Exacta:ART

LPG Gas Leak Detection System and LPG Fire Classification Based on Internet of Things and Artificial Intelligence with Telegram Bot as a Monitoring Tool Adjhi, Dhimaz Purnama; Universitas Pendidikan Indonesia Hanafi, Mohamad Rizal; Universitas Pendidikan Indonesia Suteddy, Wirmanto; Universitas Pendidikan Indonesia LPG gas leaks pose a serious threat in industrial kitchens as they can cause costly fires, both in terms of material and safety. To improve safety, an accurate detection system is required. This research focuses on developing an LPG gas leak detection system and LPG fire classification with Internet of Things and Artificial Intelligence technology. Supported by Telegram Bot as an emergency notification monitoring tool, this system uses MQ-2 sensors to detect LPG gas leaks and ESP32-Cam to classify LPG fires along with Pretrained-model technology such as Cascade Fire Detection on OpenCV Cloud Server. As the output of this system, the use of PWM control and automation oversees regulating the Exhaust Fan according to the detected leakage. FreeRTOS is also used for system task efficiency, and Port Forwarding with Ngrok Local Server allows public access to the ESP32-Cam. System testing was conducted by Black-Box testing, then evaluating the performance of the MQ-2 sensor against 400 ppm and 1500 ppm thresholds for LPG testing distances in open kitchens and closed kitchens, as well as analyzing system response and delay via HTTP protocol. The results demonstrated the system's success in detecting gas leaks, classifying LPG fires and facilitating emergency communication. LPPM 2024-09-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/21920 10.30998/faktorexacta.v17i3.21920 Faktor Exacta; Vol 17, No 3 (2024); 241-250 Faktor Exacta; Vol 17, No 3 (2024); 241-250 2502-339X 1979-276X 10.30998/faktorexacta.v17i3 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/21920/7100 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/21920/4598 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/9330 2021-08-10T19:35:25Z Faktor_Exacta:ART

Penerapan Digitalisasi Alur Bisnis Menggunakan Digital Signature Pada Salah Satu Bank di Indonesia Menggunakan Metode Scrum Purnomo, Elvan Mahardika Agustina, Fenni; Universitas Gunadarma Technological developments have led the Bank to always develop innovations that can help customers and employees to be faster in all matters, including the validation of the User ID registration form. Form validation using an ordinary signature is what has been known so far. After that, the signed registration form is sent using the Tracking System and will be processed by the User ID Management Team by reprinting it and then being signed again by Hello, the Supervisor, and the Back Office from the User ID Management Team as a sign that the application is approved. This manual method can no longer be used during this pandemic due to limited work time, the workplace, and the possibility of contracting the virus through paper. The implementation of digitizing business process flows using a digital signature is one way that activities continue to run. The application for submitting the flow of forms to digital was developed for this, as well as the change of ordinary signatures to digital signatures. This study uses a system development method using the Scrum Model. The result of developing this system is the application of a digital signature and a role application so that each role has a different function and task in the application in the process of digitizing the User ID registration form. LPPM Elvan Mahardika Purnomo Fenni Agustina 2021-08-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9330 10.30998/faktorexacta.v14i2.9330 Faktor Exacta; Vol 14, No 2 (2021); 72-83 Faktor Exacta; Vol 14, No 2 (2021); 72-83 2502-339X 1979-276X 10.30998/faktorexacta.v14i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9330/4153 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/12977 2022-11-01T15:12:13Z Faktor_Exacta:ART

ANALISIS DAN PEMODELAN PELIMPAHAN PERKARA PIDANA MENGGUNAKAN BUSINESS PROCESS IMPROVEMENT (Studi Kasus: PENGADILAN NEGERI XYZ) Awalludin, Dudi; STMIK Rosma In line with the progress of the times with the progress of developing science and technology, computers are not only used as business data development tools but also use means of processing data data quickly, accurately, and systematically. At present the business process system in Criminal Case Delegation at the XYZ District Court is still ineffective in the case registration approval process. In the trial of receiving a case transfer from the Public Prosecutor there were still some shortcomings, often double checking, there was no sign of court thanksgiving when it was transferred to the Young Criminal Registrar so that it could not be controlled again. Based on the problems obtained, the study analyzed existing transitions using qualitative using Business Process Improvement (BPI) and modeled using Business Process Modeling Notation (BPMN). to see in terms of internal and external factors. After analysis, make a checklist and receipt when delegating it to the Young Registrar of Criminal agar in the process of transferring a controlled file. Therefore, with the approval of the checklist, it can save time in performance. LPPM 2022-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12977 10.30998/faktorexacta.v15i3.12977 Faktor Exacta; Vol 15, No 3 (2022); 162-173 Faktor Exacta; Vol 15, No 3 (2022); 162-173 2502-339X 1979-276X 10.30998/faktorexacta.v15i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12977/5144 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/16703 2023-11-10T10:47:30Z Faktor_Exacta:ART

Analisis Sentimen Pindah Ibu Kota Negara (IKN) Baru pada Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine (SVM) Siregar, Amril Mutoi; Universitas Buana Perjuangan Karawang Pemindahan Ibu Kota Negara (IKN) Indonesia merupakan salah satu topik yang sedang menjadi sorotan bahkan trending topik di Twitter, sehingga menimbulkan pro kontra bagi masyarakat. Topik tersebut sudah menjadi sumber perdebatan bagi pengguna Twitter. Untuk mengetahui para pengguna twitter dalam mengemukakan pendapatnya dapat dilakukan dengan cara analisis sentimen, dimana cara tersebut memisahkan opini berdasarkan positif dan negatif. Pada analisis sentimen, metode yang digunakan biasanya menggunakan Naïve Bayes dan Support Vector Machine (SVM). Dengan dilakukannya analisa sentimen pada pemindahan IKN Indonesia dengan menggunakan dua metode algoritma yaitu Naïve Bayes dan SVM, maka permasalahan yang menjadi kontroversi dapat diketahui, sehingga dapat menjadi bahan evaluasi untuk kepentingan lainnya. Selain itu juga dengan penggunaan dua metode algoritma tersebut diharapkan dapat diketahui metode algoritma mana yang dapat menunjukkan tingkat akurasi yang tepat. Berlandaskan uraian tersebut, maka penelitian kali ini perlu memberikan kontribusi baru dalam mengalisis sentimen IKN Indonesia dengan menggunakan dua metode yang berbeda, sehingga penelitian berbeda dari penelitian-penelitian terdahulu. Penelitian ini bertujuan untuk menganalisis dan mengetahui sentimen masyarakat Indonesia terhadap pemindahan IKN melalui cuitan pada aplikasi Twitter. Untuk melakukan analisis sentimen tersebut, peneliti menggunakan dataset dari Twitter guna mengetahui perbandingan keakurasian diantara dua metode yang digunakan yaitu Naïve Bayes untuk mengkategorikan cuitan kedalam 2 kategori yaitu cuitan positif dan negatif, kemudian dibandingkan dengan metode SVM. Penelitian dilaksanakan sebagai pendukung informasi yang akurat kepada masyarakat terhadap Ibu Kota Negara. Metode penelitian yang digunakan yaitu klasifikasi Naïve Bayes dan klasifikasi SVM dengan dukungan tools Rapidminer. Hasil analisis sentimen dengan algoritma Naïve Bayes menghasilkan akurasi 86.94% memiliki nilai presisi rata-rata 96.24%, dan nilai recall 86.66%. Sedangkan hasil analisis dengan algoritma SVM menghasilkan nilai akurasi sejumlah 90.81%. Hasil analisis sentimen penelitian ini memiliki nilai presisi rata-rata sebesar 90.12%, dan nilai recall sebesar 99.12%. LPPM 2023-10-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/16703 10.30998/faktorexacta.v16i3.16703 Faktor Exacta; Vol 16, No 3 (2023) Faktor Exacta; Vol 16, No 3 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/16703/6120 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/16703/3350 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/20878 2024-07-17T04:29:56Z Faktor_Exacta:ART

Membaca Sinyal Electroencephalogram (EEG) Dalam Menangkap Tingkat Emosi (Berdasarkan Ontologi) Devianto, Yudo; Universitas Mercu Buana Sediyono, Eko; Universitas Kristen Satya Wacana Prasetyo, Sri Yulianto Joko; Universitas Kristen Satya Wacana Manongga, Danny; Universitas Kristen Satya Wacana Philosophically based EEG (electroencephalography) signal data processing is an engaging interdisciplinary approach and opens up new perspectives in understanding brain function. In this context, it is necessary to examine data from a technical or biological point of view and consider its metaphysical, epistemological and even ontological aspects. Ontology is a branch of metaphysics that deals with objects and the types of objects that exist according to one's metaphysical (or even physical) theory, their properties, and their relationship. This article attempts to provide a philosophical view of science based on ontology for processing EEG signal data, the data source of which is taken from brain waves. With the results of trials using the Artificial Neural Network (ANN) classification, an accuracy value of 46.73 was obtained. The Convolutional Neural Network (CNN) algorithm can also be used to process EEG signal data to determine a person's emotional level; this is proven in research results; although the overall accuracy of emotion recognition has increased significantly, several problems cause low accuracy in the DEAP and DREAMER data sets. There are also results of other experiments carried out using CNN, and the experimental results show that the weight of channels related to emotions is greater than that of different channels. The Continuous Capsule Network (CCN) algorithm and Deep Neural Network (DNN) algorithm can also be used to process EEG signal data to determine the level of emotion. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20878 10.30998/faktorexacta.v17i2.20878 Faktor Exacta; Vol 17, No 2 (2024); 152-160 Faktor Exacta; Vol 17, No 2 (2024); 152-160 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20878/6796 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/7964 2021-02-18T00:15:09Z Faktor_Exacta:ART

KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP KEBIJAKAN KARTU PEKERJA DI INDONESIA Anggraini, Winda Putri Utami, Manda Syari; Politeknik Statistika STIS Masalah pengangguran merupakan salah satu masalah makro ekonomi yang menjadi penghambat dalam pembangunan suatu negara atau daerah. Tingkat Pengangguran Terbuka (TPT) sejak tahun 2011 dan awal tahun 2020 mengalami penurunan tetapi cenderung melambat. Padahal, pemerintah Indonesia melalui Badan Perencanaan Pembangunan Nasional (Bappenas) menargetkan tingkat pengangguran bisa makin mengecil menjadi di bawah 4% pada tahun 2024. Salah satu program pemerintah, yang dicanangkan sejak awal pemerintahan 2019, untuk mengurangi tingkat pengangguran adalah dengan menerbitkan kartu prakerja. Selain itu juga menjadi salah satu solusi untuk menstimulasi para pekerja yang di-PHK, ataupun orang-orang yang mengalami kesulitan mencari pekerjaan pada masa pandemi. Pro dan kontra mengenai kartu prakerja terus bergulir dalam berbagai macam media. Twitter menjadi salah satu media sosial yang digemari oleh banyak masyarakat dunia termasuk di Indonesia dalam menyampaikan aspirasi, kegemaran, dan pendapatnya. Pro kontra yang hangat dibincangkan di twitter mengenai kartu prakerja menjadi hal yang perlu diperhatikan untuk penyempurnaan kebijakan tersebut. Untuk menganalisis respon masyarakat dengan menggunakan data Twitter, dapat dilakukan dengan analisis sentiment menggunakan metode pengklasifikasian Naïve Bayes. Dari model pengklasifikasian data original, training ataupun testing diperoleh hasil persentase respon berupa sentimen negatif terkait kartu prakerja adalah 52.87% lebih besar dibandingkan persentase sentiment positif sebesar 47.13%. Dan juga didapatkan nilai akurasi sebesar 91.06% dari keseluruhan tweet yang diuji. LPPM 2021-02-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7964 10.30998/faktorexacta.v13i4.7964 Faktor Exacta; Vol 13, No 4 (2020); 255-261 Faktor Exacta; Vol 13, No 4 (2020); 255-261 2502-339X 1979-276X 10.30998/faktorexacta.v13i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/7964/3717 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/12040 2022-07-14T11:58:50Z Faktor_Exacta:ART

Implementasi Metode Quantitative dan Qualitative Pada Risk Analysis & IT Risk Management Syaputra, Asep; Institut Teknologi Pagar Alam Muslim, Buhori; Universitas Putra Indonesia (UNPI) Cianjur, The purpose of this study is to produce blue prints based on the level that positively and negatively affects hardware and software in one of the Agencies in City which will later become a benchmark to avoid or overcome problems that will be faced in the IT governance and IT infrastructure. IT governance is a process relationship structure that guides and controls an organization to achieve its vision and mission by creating value that balances risk with IT and its processes. An IT facility is an entity that performs the administrative and management functions of all IT applications in the Department XYZ environment for protection against unwanted threats that require risk management assessment. Minimize the danger or risk that may arise. The two analytical methods used in this study are quantitative and qualitative risk analysis. In the future, the quantitative risk analysis (QRA) approach will focus more on analyzing the condition of IT assets to find risk factors that need serious consideration and handling. For qualitative risk analysis methods, NIST SP 80030 is used to analyze various threat and risk attributes for to provide guidelines for the management of IT facilities in Department XYZ. Based on the QRA risk assessment, it was concluded that server-class IT resources are counted as the biggest potential loss to the Service. This is reflected in the risk aspect, where power losses have the most potential damage. Qualitative assessment of risk management according to NIST SP 80030 found that the sources of high-risk threats are high-risk power grids and the Internet. This level of risk can be identified during the threat source classification process. Submission of all risk analysis results can provide the results of risk recommendations communicated with departement IT management. To then be able to help the campus make decisions that include policies, procedures, budgets, operating systems and change management. LPPM 2022-06-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12040 10.30998/faktorexacta.v15i1.12040 Faktor Exacta; Vol 15, No 1 (2022) Faktor Exacta; Vol 15, No 1 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i1 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12040/4782 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15147 2023-03-25T15:12:03Z Faktor_Exacta:ART

Penggunaan Smartphone Berbasis Android Dalam Penerapan Location Based Service Pada Absensi Karyawan Dengan Metode OOAD Wiyanto, Wiyanto; Universitas Pelita Bangsa Edora, Edora; Universitas Pelita Bangsa One of the most important activities in a company is the presence and absence of employees, this needs to be done for the continuity of activities within the company, the current system at PT. XYZ for attendance activities uses a finger print system, while for absenteeism it still uses a manual system, problems often occur in the finger print machine and are still conventional. The purpose of this study is to implement an automated employee attendance system using an Android-based location-based service so that it can make it easier for employees to perform attendance and absenteeism. This application system was developed using the OOAD (Object Oriented Analysis and Design) method, designed using UML (Unified Modeling Language) with tools from Android Studio and tested using blackbox testing LPPM DPPM, Universitas Pelita Bangsa 2023-03-23 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15147 10.30998/faktorexacta.v16i1.15147 Faktor Exacta; Vol 16, No 1 (2023) Faktor Exacta; Vol 16, No 1 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15147/5529 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/22265 2024-05-03T08:19:09Z Faktor_Exacta:ART

PENERAPAN ALGORITMA NAIVE BAYES CLASSIFIER UNTUK ANALISIS SENTIMEN KOMENTAR TWITTER PROYEK PEMBAGUNAN IKN Zamzami, Faiz; Department of Data Science, Univesitas Putra Bangsa, Indonesia Hidayat, Rahmat; Department of Informatic, Univesitas Putra Bangs, Indonesia Fathonah, Rina; Department of Data Science, Univesitas Putra Bangsa, Indonesia Recently, the relocation of Indonesia's capital city has become a hot topic of discussion among the public. Various opinions emerged regarding this mega project proposed by President Joko Widodo. Especially on social media, Twitter has become one of the most popular platforms in Indonesia as a forum for expressing people's opinions in public. In the context of the development of IKN Nusantara, researchers conducted an analysis of Twitter users' comments on President Joko Widodo's official account. Using the Naïve Bayes method with a dataset containing 220 comments consisting of 116 negative comments, 70 positive comments and 34 neutral comments. In this research, researchers developed a Python-based machine learning program. The analysis results show respective values of Precision 62%, Recall 66%, and f1-Score 63% with an accuracy level of 66%. In testing using 20% of the data, the program successfully predicted 20 negative comments, 8 neutral comments, and 16 positive comments. LPPM 2024-05-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22265 10.30998/faktorexacta.v17i1.22265 Faktor Exacta; Vol 17, No 1 (2024) Faktor Exacta; Vol 17, No 1 (2024) 2502-339X 1979-276X 10.30998/faktorexacta.v17i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22265/6607 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/23347 2024-10-30T05:13:35Z Faktor_Exacta:ART

Klasifikasi Tingkat Kemanisan Buah Kersen Berdasarkan Fitur Warna NTSC Menggunakan Jaringan Syaraf Tiruan Berbasis Pengolahan Citra Digital Rusli, Risvan; Universitas Negeri Makassar Fachriansyah, Zaky; Universitas Negeri Makassar Ilham, Muh; Universitas Negeri Makassar Kaswar, Andi Baso; Universitas Negeri Makassar Andayani, Dyah Darma; Universitas Negeri Makassar The fruit of the calabura tree (Muntingia calabura) is a small red fruit originating from the Prunus genus, often found along roadsides. This fruit contains numerous nutrients beneficial for bodily health, serving as a highly potential source of nutrition. Presently, a challenge exists in determining the sweetness level of calabura fruit, relying heavily on manual human assessment. The development of classification utilizing technology is considered a crucial step. Previous research has concentrated on classifying various objects using RGB, HSV, YCbCr color feature extraction. However, it was observed that RGB, HSV, YCbCr color features are not universally suitable, particularly for calabura fruits. Hence, this study employs a method of classifying the sweetness level of calabura fruit based on NTSC color features using a Digital Image Processing-based Artificial Neural Network (ANN). This approach leverages color-based image processing features. The research involves several stages, starting from acquiring 300 calabura fruit images with 3 levels of classification to the classification process utilizing Backpropagation in the ANN. Multiple training and testing scenarios were conducted to select feature combinations with the highest accuracy and fastest computational time. Results revealed that the most effective feature used was the NTSC color feature as a skin characteristic parameter. Based on training outcomes using 210 training images, the accuracy reached 100% with a computational time of 1.66 seconds per image. Meanwhile, testing with 90 sample images showed an accuracy of 94% with a computational time of 4.23 seconds per image. Thus, it can be concluded that the employed method successfully classifies the quality of calabura fruit images based on color features and skin characteristics. LPPM 2024-10-28 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23347 10.30998/faktorexacta.v17i3.23347 Faktor Exacta; Vol 17, No 3 (2024); 295-305 Faktor Exacta; Vol 17, No 3 (2024); 295-305 2502-339X 1979-276X 10.30998/faktorexacta.v17i3 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/23347/7105 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/11259 2022-01-26T13:47:30Z Faktor_Exacta:ART

Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media Agustina, Nova; Sekolah Tinggi Teknologi Bandung Adrian, Adrian; Sekolah Tinggi Ilmu Ekonomi Jayakarta Hermawati, Mercy; Universitas Indraprasta PGRI Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "satrawi". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface. Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "satrawi". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface. LPPM 2022-01-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11259 10.30998/faktorexacta.v14i4.11259 Faktor Exacta; Vol 14, No 4 (2021); 206-213 Faktor Exacta; Vol 14, No 4 (2021); 206-213 2502-339X 1979-276X 10.30998/faktorexacta.v14i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/11259/4503 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/15286 2023-01-24T12:06:17Z Faktor_Exacta:ART

Konfigurasi Hyperparameter Long Short Term Memory untuk Optimalisasi Prediksi Penjualan Khumaidi, Ali; Universitas Krisnadwipayana Tari, Dhistianti Mei Rahmawan; Universitas Krisnadwipayana Chusna, Nuke L.; Universitas Krisnadwipayana To support business development and competition, forecasting capabilities with good accuracy are required. PT. Sumber Prima Inti Motor does not want the customer's spare part needs not to be available when ordered, therefore an appropriate procurement and sales forecasting strategy is needed. Long Short Term Memory (LSTM) is a fairly good algorithm for forecasting, in this study using LSTM to predict sales of spare parts for the next 60 days. The CRISP-DM method is used and to obtain optimal model performance, hyperparameter configuration is performed. The configurations used are number of hidden layers, data partition, epoch, batch size, and dropout scenario. The best results from the LSTM model hyperparameter configuration are 3 hidden layers, 3 dropouts, epoch 150, and batch size 30. The performance of the training and testing models with RMSE is 0.0855 and 0.0846. LPPM 2023-01-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15286 10.30998/faktorexacta.v15i4.15286 Faktor Exacta; Vol 15, No 4 (2022); 290-300 Faktor Exacta; Vol 15, No 4 (2022); 290-300 2502-339X 1979-276X 10.30998/faktorexacta.v15i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/15286/5357 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/20073 2024-01-09T11:01:49Z Faktor_Exacta:ART

Penerapan Metode Convolution Neural Network (CNN) Dalam Proses Pengolahan Citra Untuk Mendeteksi Cacat Produksi Pada Produk Masker Arvio, Yozika; Institut Teknologi PLN Kusuma, Dine Tiara; Institut Teknologi PLN Sangadji, Iriansyah BM; Institut Teknologi PLN Dewantara, Erno Kurniawan; Universitas Budi Luhur A mask manufacturer in Indonesia with a production of 4 million masks per day for various types of masks. However, in the production process there are still many defective and unsalable masks that enter the stock of goods to be sent, this is due to the quality control process that is still manual. So that to reduce product defects, it is necessary to mitigate by creating a system that can detect defective products, to facilitate the quality control process, an intelligent computing system is needed so that it is expected to reduce mask production defects to build this computational model will be carried out in several stages. The first stage will be a field study to obtain samples of defective and perfect products. The second stage builds a computational model, this model is built based on the Convolution Neural Network (CNN) method and the third stage builds a system that suits the needs in the field and tests the system against the company's needs. The purpose of this research is to produce a good and perfect defective product detection system so that it can be useful for reducing defective products that pass the quality control stage. From this research, if the process is run by entering existing data, it produces an accuracy percentage of 99% of the 750 data tested. While in real time testing, a percentage of 96.4% was obtained using 28 data. LPPM 2024-01-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20073 10.30998/faktorexacta.v16i4.20073 Faktor Exacta; Vol 16, No 4 (2023) Faktor Exacta; Vol 16, No 4 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/20073/6295 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/20073/4127 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/6513 2020-12-17T16:04:44Z Faktor_Exacta:ART

Membangun Pythagoras Sebagai Visualisasi Random Forest Untuk Pemodelan Pohon Keputusan Ambarsari, Erlin Windia; Universitas Indraprasta PGRI Herlinda, Herlinda; Universitas Indraprasta PGRI Students observed Pythagoras for using a plane Geometry and 3D Geometry. However, Pythagoras can also be built for decision trees. Our research regarding Instagram Usage Habit with construct Pythagoras for a single decision tree. The study's results obtained are ambiguous attribute values. Therefore, it is continued with research to build Pythagoras for Random Forest. The purpose of the study is to facilitate the tracking of ambiguous data contained in the attributes. The results obtained that the relationship between characteristics of the target class, thus resulting in misclassification. This error caused invalid data; for example, there are three times the separation of data on the same attribute for age's target for a group of 20. However, although there are misclassifications caused by invalid data, based on the Pythagorean construction for Random Forest, the data is more easily traced to errors, which cannot be done by a single decision tree. LPPM 2020-11-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6513 10.30998/faktorexacta.v13i3.6513 Faktor Exacta; Vol 13, No 3 (2020) Faktor Exacta; Vol 13, No 3 (2020) 2502-339X 1979-276X 10.30998/faktorexacta.v13i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/6513/3532 Copyright (c) 2020 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/9567 2021-12-24T14:22:10Z Faktor_Exacta:ART

Analisis Model Pengukuran Tinggi Permukaan Air Dengan Metode Canny Edge Detection dan Image Contouring Sebagai Indikator Peringatan Dini Bencana Banjir Alexander, Frederick; Universitas Budi Luhur Imelda, Imelda; Universitas Budi Luhur Flood disaster remains a natural phenomenon that often occurs in Indonesia, especially in the Wisma Tajur Housing Complex area, Tangerang City which causes property losses including the safety of the souls of the affected community. The difficulty experienced so far is how to measure the water level to obtain alert status information as an indicator of flood warning. As a solution in overcoming these problems, this research proposes a method based on digital image processing with canny edge detection algorithms and image contouring in an effort to measure river water level. Canny edge detection and image contouring were chosen due to their accuracy in detecting the edges of the image and the ease of the computation process. The steps taken in this research are to conduct a simulation experiment of measuring the water level using a water container that can describe the situation in the river, then doing field testing. Canny edge detection produces an outline which can then be detected by the contour, then water level measurements can be made on the bounding rectangle that is formed and changes dynamically with fluctuations in water level. The contribution of this research is the use of black measuring lines that are processed using thresholding techniques to facilitate the process of measuring water level using a combination of canny edge detection and image contouring techniques as well as adding attributes / features using threshold, MinVal, and MaxVal values on the canny edge. Sampling testing produces an accuracy of 99.96%, prototype testing produces 100% accuracy, and direct testing produces an accuracy of 99.85%. LPPM 2021-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9567 10.30998/faktorexacta.v14i3.9567 Faktor Exacta; Vol 14, No 3 (2021); 117-130 Faktor Exacta; Vol 14, No 3 (2021); 117-130 2502-339X 1979-276X 10.30998/faktorexacta.v14i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/9567/4321 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/14795 2023-01-24T12:06:17Z Faktor_Exacta:ART

Perbandingan Metode Euclidean Distance dan Haversine Distance pada Aplikasi Sistem PPDB dan algoritma K-Means Untuk Menentukan Kebijakan Peraturan Zonasi Syarifudin, Mustofa Kamal; Universitas Nasional Komala Sari, Ratih Titi; Universitas Nasional At this time, registration for public schools is straightforward to do online with a device via a web browser without the need to install an application that can ease the device's performance. Still, the government regulates it through a zoning policy that makes students register for schools close to their homes. This study examines and compares which Euclidean and Haversine algorithms are more accurate to implement in making an application that determines the distance between the school and the student's house. Then the school will decide which students can be accepted using the K-Means algorithm, as has been done by SMPN 1 Tigaraksa, which results that the haversine algorithm has an average accuracy rate of 99.71%, an average error of 0.29% with an average distance difference of 1.86 meters. In comparison, Euclidean has an accuracy rate of 99.65%, an average -the average error is 0.35% with the difference in average distance at the actual length of 2.42 meters. Therefore, the difference in distance between the two algorithms obtained is 1.27 meters. And the K-Means Algorithm can be the proper method for making decisions because the algorithm groups according to the farthest, medium, and closest distances LPPM Ratih Titi Komala Sari, Universitas Nasional 2023-01-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/14795 10.30998/faktorexacta.v15i4.14795 Faktor Exacta; Vol 15, No 4 (2022); 206-212 Faktor Exacta; Vol 15, No 4 (2022); 206-212 2502-339X 1979-276X 10.30998/faktorexacta.v15i4 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/14795/5348 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/17686 2023-11-10T10:42:16Z Faktor_Exacta:ART

Sistem Pakar Diagnosa Penyakit Berisiko Di Setiap Status Gizi Berdasarkan Indeks Massa Tubuh Menggunakan Metode Hy-brid Case Base Pratama, Rifqhy Rayhan Andi Riga; Amikom Yogyakarta University Norhikmah, Norhikmah Nutritional status is an indicator of health for a person which is divided into four classifications, namely, Obesity, Overweight, Normal Weight, and Underweight. and nutritional status can be obtained by calculating the body mass index (BMI) of the person by dividing the body weight (kg) by the height (m2). after knowing the nutritional status, a diagnosis can be made by applying the Hybrid Case Base method. Applicationing of this method is too precise because it can provide accurate diagnostic results because this method combines two methods, namely Case Based Reasoning and Rule Based Reasoning. With this expert system, it can help people to find out their nutritional status and whether they have a risky disease or not, especially for people who don't have a lot of free time to consult with doctors so they can do it only by using the expert system that has been created. This method is quite fast and practical and provides accurate diagnostic results as well. LPPM Universitas AMIKOM Yogyakarta 2023-10-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17686 10.30998/faktorexacta.v16i3.17686 Faktor Exacta; Vol 16, No 3 (2023) Faktor Exacta; Vol 16, No 3 (2023) 2502-339X 1979-276X 10.30998/faktorexacta.v16i3 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/17686/6115 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/17686/3532 Copyright (c) 2023 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/22760 2024-07-17T04:29:56Z Faktor_Exacta:ART

Sistem Pendukung Keputusan Penerima Bantuan Renovasi Rumah Pasca Gempa Cianjur Menggunakan Multi Attribute Decision Making dengan Metode SAW Maskur A, Moch Riyadi; Budi Luhur University Triyono, Gandung; Budi Luhur University Dalam situasi darurat bencana diperlukan keefektifan dan efisiensi dalam pengambilan keputusan. Dalam penelitian ini yaitu keputusan untuk menentukan penerima bantuan renovasi rumah pasca gempa. Pada saat ini, salah satu permasalahan adalah ketidakakuratan dalam pengambilan keputusan. Hal tersebut dapat menyebabkan ketidaktepatan dalam pemberian bantuan. Adanya permasalahan tersebut, maka diperlukan sebuah sistem pendukung keputusan menentukan penerima bantuan renovasi pasca gempa. Pada penelitian ini diusulkan sebuah model sistem pendukung keputusan terbaik dengan pendekatan MADM dengan metode SAW. Model yang dikembangkan menggunakan 8 kriteria, yaitu kondisi pondasi, kondisi sloof, kondisi penutup atap, kondisi rangka atap, kondisi lantai, kondisi dinding, kondisi ring balok, dan kondisi kolom. Pada penelitian ini digunakan 20 data calon penerima bantuan, dengan rincian 5 data yang termasuk rumah dengan renovasi kategori rusak berat, 9 data untuk renovasi rumah yang rusak sedang, 4 data untuk renovasi rumah yang rusak ringan, dan 2 data untuk yang tidak layak mendapatkan bantuan renovasi rumah. Hasil pengujian model dihasilkan akurasi 98% untuk akurasi nilai, dan 95% untuk akurasi kelayakan yang dibandingkan dengan data aktual yang didapat. LPPM 2024-07-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22760 10.30998/faktorexacta.v17i2.22760 Faktor Exacta; Vol 17, No 2 (2024); 199-211 Faktor Exacta; Vol 17, No 2 (2024); 199-211 2502-339X 1979-276X 10.30998/faktorexacta.v17i2 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/22760/6801 Copyright (c) 2024 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/8989 2021-04-27T14:57:10Z Faktor_Exacta:ART

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CNN UNTUK SISTEM PENGENALAN WAJAH Dewi, Noviana Ismawan, Fiqih; Universitas Indraprasta PGRI Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library open face. The result of this research is applying deep learning with the CNN method to classification process that resulting percentage of precision of 96%, recall percentage of 100%, and accuracy percentage of 99.8%. LPPM 2021-03-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8989 10.30998/faktorexacta.v14i1.8989 Faktor Exacta; Vol 14, No 1 (2021); 34-43 Faktor Exacta; Vol 14, No 1 (2021); 34-43 2502-339X 1979-276X 10.30998/faktorexacta.v14i1 ind https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/8989/3946 Copyright (c) 2021 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0

oai:ojs.localhost:article/12894 2022-08-10T14:48:49Z Faktor_Exacta:ART

Perancangan Sistem Penerimaan Siswa Baru Berbasis Web Pada Sekolah Dasar Islam Plus Baitul Maal Herfandi, Herfandi; Universitas Teknologi Sumbawa Dwiasnati, Saruni; Universitas Mercu Buana Baihaqi, Kiki Ahmad; Universitas Buana Perjuangan Karawang Avrizal, reza; Universitas Indraprasta PGRI Islamic-based education in Indonesia is an educational institution that focuses on forming the character and knowledge of Islamic religious values. Islamic Elementary School Education Institute Plus Baitul Maal in carrying out educational administration procedures still uses the conventional system. The procedure that took place has not been efficient because prospective students are required to complete the registration form sheet by manual means and still stored in the folder. The implementation of information system technology in the education sector is able to provide convenience, especially in terms of efficientness, accuracy and novelty of information. Therefore, the design and creation of a website-based new student admission information system is expected to be able to solve the problem. This research resulted in a website-based new student admission information system with the main Page having Home, About Us, How to Apply, and Contact functionality. The student dashboard page has Home, Student Profile, Document functionality. The admin dashboard page has the functionality of Home, Website Content, Management, Document Completeness, and Settings as an admin user management serves to set up admin accounts (Create, Record, Update and Delete (CRUD) for admin accounts), development methods using waterfalls, research methods using qualitative and information system testing using black box testing that gets conclusions according to various functionality tests. This system is expected to ease the work of new student admission administrators as well as adjustments to education in the Industrial 4.0 era. LPPM 2022-08-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12894 10.30998/faktorexacta.v15i2.12894 Faktor Exacta; Vol 15, No 2 (2022) Faktor Exacta; Vol 15, No 2 (2022) 2502-339X 1979-276X 10.30998/faktorexacta.v15i2 eng https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/view/12894/4918 https://journal.lppmunindra.ac.id/index.php/Faktor_Exacta/article/downloadSuppFile/12894/2374 Copyright (c) 2022 Faktor Exacta http://creativecommons.org/licenses/by-nc/4.0 5de4ef41cdd00bf206a5279f10b8f724