Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote

nanang ruhyana(1*), didi rosiyadi(2)

(1) 
(2) LIPI
(*) Corresponding Author

Abstract


Customer satisfaction is one of the things expected by a company when the product produced has been marketed, both in the form of goods and services. How to complain through customer service is very diverse, lately not only by telephone, customers submit their suggestions or complaints. Customers can submit their suggestions or complaints via e-mail or e-mail or forums in the virtual world that are made by product-producing companies to accommodate a variety of complaints, suggestions, and direct criticism from consumers, especially social media, who are free to express their opinions on shipping services. they use. Instagram is a social media that is more inclined to images and on the other hand has text captions and comments, from the above problems trying to make a research for customer complaints of users of goods delivery services on an Instagram account shipping service company. From the background of the problem, the researchers tried to solve the problem for text mining classifiers by using the Support Vector Machine (SVM) and Naïve Bayes methods and using the SMOTE technique with the usual processes for text mining so that they could produce 69.68% accuracy for Support Vector Machine (SVM) and Naïve Bayes with an accuracy of 88.54%, using the Instagram comment text dataset of 776 records that have been done with preprocessing text.

Full Text:

PDF (Indonesian)

References


Arwan, Ardina, V., Ariana, L. R., Samuel, F., Ramdani, D., Aditya, & Sukmana, E. A. (2018). Synthetic Minority Over-Sampling Technique (Smote) Algorithm For Handling Imbalanced Data.

Bramer, M. (2007). Principles of Data Mining. Springer London.

Dellia, P., & Tjahyanto, A. (2017). Tax Complaints Classification on Twitter Using Text Mining. 2(1).

Dewi, R. N. (2018). Model Text Mining Untuk Identifikasi Keluhan Pelanggan Produk Perusahaan Perangkat Lunak.

Essra, A., Rahmadani, & Safriadi. (2016). Analisis Information Gain Attribute Evaluation Untuk Klasifikasi Serangan Intrusi. Journal of Information System Development, 2(2), 9–14.

Frastian, N., Hendrian, S., & V.H.Valentino. (2018). Implementasi komparasi algoritma klasifikasi menentukan kelulusan mata kuliah algoritma universitas budi luhur. (August), 0–8. https://doi.org/10.13140/RG.2.2.31821.74721

Han, J., Kamber, M., & Jian, P. (2012). Data Mining: Concepts and Techniques. In San Francisco, CA, itd: Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-381479-1.00001-0

Hartini, S. (2016). Efektifitas Endorsment Pada Media Sosial Instagram Pada Produk Skin Care. 3(1), 43–50.

Indriyani, S., & Mardiana, S. (2016). Pengaruh Penanganan Keluhan ( Complaint Handling ) Terhadap Kepercayaan Dan Komitmen Mahasiswa Pada Perguruan Tinggi Swasta Di Bandar Lampung. 2(01), 1–13.

Irfansyah, P. (2016). Kajian Komparasi Penerapan Algoritma Data Mining (C4.5, Bayesian Classifier Dan Neural Network) Dalam Menentukan Promosi Jabatan. Prosiding Seminar Nasional, 53–67.

Laksana, J., & Purwarianti, A. (2015). Indonesian Twitter text authority classification for government in Bandung. Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014, 129–134. https://doi.org/10.1109/ICAICTA.2014.7005928

Novianti, K. D. P., Setiawan, N. A., & Kusumawardani, S. S. (2015). Peningkatan Nilai Recall dan Precision pada Penelusuran Informasi Pustaka Berbasis Semantik ( Studi Kasus : Sistem Informasi Ruang Referensi Jurusan Teknik Elektro dan Teknologi Informasi UGM). 9–10.

Sabirin, & Setiawati, C. I. (2017). The driving factors of instagram utilization for marketing efforts in promoting student owned online store. Proceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016, 64–69. https://doi.org/10.1109/ISEMANTIC.2016.7873811

Widagdo, P. B. (2016). Perkembangan Electronic Commerce ( E- Commerce ) di Indonesia. (December), 10.

Zhu, X. (2017). Agile mining : a novel data mining process for industry practice based on Agile Methods and visualization.




DOI: http://dx.doi.org/10.30998/faktorexacta.v12i4.4981

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

DOAJ faktor exacta Garuda ISSN BRIN sinta

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Flag Counter

site
stats View Faktor Exacta Stats


pkp index