Analisis dan Visualisasi Berbasis Web Sentimen Pengguna Jenius Menggunakan Naïve Bayes Classifier

Authors

  • Jimmy Pratama Setiadi Universitas STIKUBANK Semarang
  • Sugiyamta Sugiyamta Universitas STIKUBANK Semarang

DOI:

https://doi.org/10.32493/jtsi.v7i1.37981

Keywords:

Sentiment analysis; Web; Naïve Bayes Classifier; Preprocessing; Word Cloud

Abstract

Along with technology development, many varied digital banking or Mobile Banking applications have emerged. Mobile Banking is an innovative result of technological advances in the banking sector and Jenius is one of them. The high level of competition urges Jenius to maintain its competitive advantage and continue to innovate so that it can continue to survive. To overcome these problems, researchers propose a sentiment analysis process to understand the wants and needs of user feedback. This research uses the Naïve Bayes Classifier algorithm as a sentiment analysis method with visualization results presented via the web. The dataset is obtained by scraping method with three sentiment categories: positive, neutral, and negative. The data processing process uses the Preprocessing method with Naïve Bayes testing performed on three configurations of training data: testing data to determine the highest accuracy. The results of this study show that the Naïve Bayes algorithm gets the highest accuracy rate of 90% with the results of positive sentiment word clouds that describe the ease of use of the application as a reference for Jenius to continue to maintain these advantages, while neutral sentiment word clouds that describe the difficulty of the verification process and negative sentiment word clouds that describe application performance that is not optimal as an improvement material for Jenius to increase satisfaction and increase customer satisfaction.

References

Apriani, R., & Gustian, D. (2019). Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia. Jurnal Rekayasa Teknologi Nusa Putra, 6(1), 54-62.

Fiarni, C., Maharani, H., & Pratama, R. (2016). Sentiment Analysis System for Indonesia Online Retail Shop Review Using Hierarchy Naive Bayes Technique. Conference: 2016 4th International Conference on Information and Communication Technology (ICoICT), 212-217. https://doi.org/10.1109/ICoICT.2016.7571912

Nugroho, D. G., Chrisnanto, Y. H., & Wahana, A. (2016). Analisis Sentimen pada Jasa Ojek Online Menggunakan Metode Naïve Bayes. Prosiding Seminar Sains Nasional dan Teknologi, 1(1), 156-161.

Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 4(2), 17-29. www.femaledaily.com

Ginting, S. L. B., & Trinanda, R. P. (2013). Teknik Data Mining Menggunakan Metode Bayes Classifier untuk Optimalisasi Pencarian pada Aplikasi Perpustakaan (Studi Kasus : Perpustakaan Universitas Pasundan – Bandung). Jurnal Teknologi dan Informasi (JATI), 3(2), 37-50. https://doi.org/https://doi.org/10.34010/jati.v3i2.794

Maulana, R., Iskandar, & Mailany, M. (2018). Pengaruh Penggunaan Mobile Banking terhadap Minat Nasabah dalam Bertransaksi Menggunakan Technology Acceptance Model. Cyberspace: Jurnal Pendidikan Teknologi Informasi, 2(2), 146-155.

Natasuwarna, A. P. (2020). Seleksi Fitur Support Vector Machine pada Analisis Sentimen Keberlanjutan Pembelajaran Daring. Techno.Com: Jurnal Teknologi Informasi, 19(4), 437-448.

Sanjaya, T. P. R., Ahmad Fauzi, & Anis Fitri Nur Masruriyah. (2023). Analisis Sentimen Ulasan pada E-Commerce Shopee Menggunakan Algoritma Naive Bayes dan Support Vector Machine. INFOTECH : Jurnal Informatika & Teknologi, 4(1), 16–26. https://doi.org/10.37373/infotech.v4i1.422

Somantri, O., & Apriliani, D. (2018). Support Vector Machine Berbasis Feature Selection untuk Sentiment Analysis Kepuasan Pelanggan terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 5(5), 537-548. https://doi.org/10.25126/jtiik20185867

Lukmana, D. T., Subanti, S., & Susanti, Y. (2019). Analisis Sentimen terhadap Calon Presiden 2019 dengan Support Vector Machine di Twitter. Seminar & Conference Proceedings of UMT, 154-160.

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 Januari 2024

Downloads

Published

2024-01-30

How to Cite

Setiadi, J. P., & Sugiyamta, S. (2024). Analisis dan Visualisasi Berbasis Web Sentimen Pengguna Jenius Menggunakan Naïve Bayes Classifier. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(1), 245–254. https://doi.org/10.32493/jtsi.v7i1.37981

Issue

Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Section

Article

License

Copyright (c) 2024 Jimmy Pratama Setiadi, Sugiyamta Sugiyamta

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.

In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License

YOU ARE FREE TO:

  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.
  • The licensor cannot revoke these freedoms as long as you follow the license terms