Analisis Trend Topik Penelitian Tesis Pada Program Studi Magister Ilmu Komputer Universitas Budi Luhur Menggunakan Metode Latent Dirichlet Allocation (LDA)

Arief Wahyudi; Luhur Bayuaji

doi:10.30998/faktorexacta.v17i1.21190

Analisis Trend Topik Penelitian Tesis Pada Program Studi Magister Ilmu Komputer Universitas Budi Luhur Menggunakan Metode Latent Dirichlet Allocation (LDA)

Arief Wahyudi^(1*), Luhur Bayuaji⁽²⁾

(1) Universitas Budi Luhur
(2) Budi Luhur University
(*) Corresponding Author

Abstract

Every year, thousands of studies are conducted by researchers from various institutions and places, focusing on various fields and topics. This also applies to thesis research conducted by students of the Master of Computer Science Program at the Faculty of Information Technology, Universitas Budi Luhur. Given the significant amount of research over time at Budi Luhur University's Master of Computer Science Program, it has become increasingly difficult to effectively understand research trends and focus. The purpose of this study is to identify trending thesis research topics in the Master of Computer Science Program at Universitas Budi Luhur. The data used in this research includes thesis research titles conducted from 2016 to 2021. The method used in this research is Latent Dirichlet Allocation (LDA). The results of the study produced the best pass value at 28 and the best number of topics was 5 topics. LDA modeling produces 5 research topics that are trending in the period 2016 to 2021, namely sentiment analysis, data analysis, prediction analysis, decision support systems and machine learning.

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References

Pasca Sarjana Budi Luhur, “Sejarah Singkat,” https://pascasarjana.budiluhur.ac.id/id/profil/sejarah-singkat/. Diakses: 7 Agustus 2023. [Daring]. Tersedia pada: https://pascasarjana.budiluhur.ac.id/id/profil/sejarah-singkat/

Setyowati Risha, “Trends Topik Penelitian Bidang Ilmu Perpustakaan (Analisis Bibliometrik-Zipfs Law Pada Abstrak Tesis Mahasiswa S2 Ilmu Perpustakaan di Universitas Gajah Mada dan UIN Sunan Kalijaga Tahun 2014-2016),” UIN Sunan Kalijaga , Yogyakarta , 2017. Diakses: 21 November 2022. [Daring]. Tersedia pada: http://digilib.uin-suka.ac.id/id/eprint/28407/1/1420010005_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf

Bahruni dan Fathurrahmad, “Analisis Trend Topik Penelitian Pada Web of Science dan SINTA untuk Penentuan Tema Tugas Akhir Mahasiswa,” Jurnal Saintekom, vol. 10, no. 1, Mar 2020, doi: https://doi.org/10.33020/SAINTEKOM.V10I1.91.

W. Wiyarsih, “Analisis Trends Topik Penelitian Mahasiswa Fakultas MIPA UGM Periode 2016-2018,” UNILIB : Jurnal Perpustakaan, vol. 12, no. 1, Feb 2021, doi: 10.20885/unilib.vol12.iss1.art1.

L. H. Suadaa dan A. Purwarianti, “Combination of Latent Dirichlet Allocation (LDA) and Term Frequency-Inverse Cluster Frequency (TFxICF) in Indonesian text clustering with labeling,” dalam 2016 4th International Conference on Information and Communication Technology, ICoICT 2016, Institute of Electrical and Electronics Engineers Inc., Sep 2016. doi: 10.1109/ICoICT.2016.7571885.

G. Xu, Y. Meng, Z. Chen, X. Qiu, C. Wang, dan H. Yao, “Research on Topic Detection and Tracking for Online News Texts,” IEEE Access, vol. 7, hlm. 58407–58418, 2019, doi: 10.1109/ACCESS.2019.2914097.

KBBI Daring, “Kamus.” Diakses: 2 Februari 2023. [Daring]. Tersedia pada: https://kbbi.kemdikbud.go.id/entri/tren)

P.-N. Tan, M. Steinbach, A. Karpatne, dan V. Kumar, Introduction to Data Mining, 2nd ed. United Kingdom : Pearson , 2019.

O. Y. Findawati, M. M. Muhammad, A. Rosid, S. Kom, dan M. Kom, Buku Ajar Text Mining, vol. cetakan I. Sidoarjo : UMSIDA Press , 2020.

M. R. Faisal, D. Kartini, A. R. Arrahimi, dan T. H. Saragih, Belajar Data Science: Text Mining Untuk Pemula I, I. Banjarbaru : Scripta Cendekia , 2022.

D. M. Blei, A. Y. Ng, dan J. B. Edu, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, hlm. 993–1022, 2003, Diakses: 11 Februari 2023. [Daring]. Tersedia pada: https://dl.acm.org/doi/10.5555/944919.944937

H. Jelodar dkk., “Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey,” 2017, Diakses: 10 Agustus 2023. [Daring]. Tersedia pada: https://www.semanticscholar.org/paper/Latent-Dirichlet-allocation-(LDA)-and-topic-models%2C-Jelodar-Wang/b22de434b462558a127f327f29e2b0c673c0d7ab

Y. Sahria, N. Isnaini Febriarini, dan P. Dwi Oktavianti, “Pemodelan Topik Penelitian Bidang Keperawatan Indonesia pada Repository Jurnal Sinta Menggunakan Metode Topic Modelling LDA (Latent Dirichlet Allocation),” dalam Prosiding-Seminar Teknologi Informasi dan Ilmu Komputer , 2020, hlm. 90–102.

D. M. Blei, “Introduction to Probabilistic Topic Models,” 2011, Diakses: 12 Februari 2023. [Daring]. Tersedia pada: http://www.cs.columbia.edu/~blei/papers/Blei2011.pdf

A. Widodo, I. Budi, dan R. Fathoni Aji, “Prediksi Topik Penelitian Menggunakan Kombinasi Antara Support Vector Regression dan Kurva Logistik,” dalam Seminar Nasional Aplikasi Teknologi Informasi, 2012.

I. M. K. B. Putra dan R. P. Kusumawardani, “Analisis Topik Informasi Publik Media Sosial di Surabaya Menggunakan Pemodelan Latent Dirichlet Allocation (LDA),” Jurnal Teknik ITS , vol. 6, no. 2, hlm. 311–316, 2017.

D. A. Navastara, E. Mursidah, Y. A. Gonti, D. Wahyuni, P. Wiyadi, dan W. Suadi, “Clustering Topik Penelitian Berbasis Unsupervised Learning Untuk Rekomendasi Koleksi Pustaka di Perpustakaan ITS,” Jurnal Ilmiah Teknologi Informasi , hlm. 125–134, 2018, doi: http://dx.doi.org/10.12962/j24068535.v17i2.a788.

Dwiyantoro dan S. Junandi, “Tren Topik Penelitian dan Kajian Bibliometrik Prosiding Bidang Ilmu Perpustakaan di Indonesia Periode 2015-2017,” 2020, Diakses: 21 November 2022. [Daring]. Tersedia pada: https://ejournal.perpusnas.go.id/mp/article/view/533

S. Bahri, D. Wintana, R. Wajhillah, dan S. Suhada, “Penerapan Metode Topic Modelling untuk Penentuan Topik Konsultasi pada Portal Telemedicine Menggunakan LDA (Latent Dirichlet Allocation),” Kumpulan Jurnal Ilmu Komputer , vol. 09, no. 03, hlm. 402–410, 2022.

K. R. Adjie, P. Santoso, A. Husna, N. W. Putri, dan A. Rakhmawati, “Analisis Topik Tagar Covidindonesia pada Instagram Menggunakan Latent Dirichlet Allocation,” Jurnal Informatika Sunan Kalijaga), vol. 7, no. 1, hlm. 1–9, Jan 2022.

Universitas Budi Luhur, “Sejarah Universitas Budi Luhur,” https://www.budiluhur.ac.id/tentang/sejarah/. Diakses: 7 Agustus 2023. [Daring]. Tersedia pada: https://www.budiluhur.ac.id/tentang/sejarah/

Fakultas Teknologi Informasi, “Struktur Organisasi ,” https://fti.budiluhur.ac.id/profil-fti/struktur-organisasi/.

DOI: http://dx.doi.org/10.30998/faktorexacta.v17i1.21190

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