Analisis Sentimen Ulasan Aplikasi Jamsostek dengan SVM, Random Forest, dan Logistic Regression
|
Keywords:
Sentiment Analysis, JMO, Support Vector Machine, TF-IDF, Machine Learning
AbstractThe digitalization of public services has encouraged the development of the Jamsostek Mobile (JMO) application by BPJS Ketenagakerjaan. This application is expected to provide convenience in accessing information, JHT claims, and other services. However, user reviews on the Google Play Store show diverse perceptions, ranging from satisfaction to technical complaints. This study aims to conduct sentiment analysis on user reviews of the JMO application by classifying opinions into positive, negative, and neutral sentiments. Data were collected through crawling from the Google Play Store and processed using text preprocessing stages, including data cleaning, case folding, stopword removal, tokenization, stemming, and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The classification process was then carried out using three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression. The results indicate that negative sentiment dominates with 46%, followed by positive sentiment at 40% and neutral at 14%. Most complaints are related to login difficulties, application errors, and technical bugs in claim features. In terms of algorithm performance, SVM with a linear kernel achieved the highest accuracy of 87.5% and an F1-score of 0.87, outperforming Random Forest (85.3%) and Logistic Regression (82.7%). Academically, this study reinforces the effectiveness of SVM in sentiment analysis using TF-IDF, while practically providing recommendations for BPJS Ketenagakerjaan to improve system stability, login speed, and reduce application bugs to enhance user satisfaction.Downloads
Download data is not yet available.
ReferencesBr Sagala, R., & Hajad, V. (2022). Inovasi Pelayanan Kesehatan Mobile JKN di Kantor BPJS Kota Subulussalam. Journal of Social Politics and Governance (JSPG), 4(1). DOI: https://doi.org/10.24076/JSPG.2022v4i1.775 . Fitriyana, V., Hakim, L., Novitasari, D. C. R., & Asyhar, A. H. (2023). Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine. Jurnal Buana Informatika, 14(01), 40–49. DOI: https://doi.org/10.24002/jbi.v14i01.6909 . Ash Shiddiqi, M. H., et al. (2023). Implementasi dalam Peningkatan Kepesertaan BPJS Ketenagakerjaan Melalui Aplikasi Jamsostek (JMO) KCP. Pasaman Barat: Memudahkan Akses Layanan E-Government. Ranah Research: Journal of Multidisciplinary Research and Development, 6(1), 9–13. DOI: https://doi.org/10.38035/rrj.v6i1.790 . Nufairi, F., Pratiwi, N., & Herlando, F. (2024). Analisis Sentimen pada Ulasan Aplikasi Threads di Google Play Store Menggunakan Algoritma Support Vector Machine. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 9(1), 339–348. DOI: https://doi.org/10.29100/jipi.v9i1.4929 . Junianto, H., Arsi, P., Kusuma, B. A., & Saputra, D. I. S. (2024). Evaluasi Aplikasi Raileo Melalui Analisis Sentimen Ulasan Playstore dengan Metode Naive Bayes. SINTECH (Science and Information Technology) Journal, 7(1), 27–40. DOI: https://doi.org/10.31598/sintechjournal.v7i1.1505 . Madao, O. E., Irsyad, A., & Ibrahim, M. R. (2025). Analisis Sentimen pada Ulasan Pengguna Aplikasi Jamsostek Mobile dengan Menggunakan Naïve Bayes dan Logistic Regression. Djtechno: Jurnal Teknologi Informasi, 6(2), 458–472. DOI: https://doi.org/10.46576/djtechno.v6i2.6775 . zarine, D., Rahaningsih, N., & Dana, R. D. (2025). Analisis Sentimen terhadap Pengguna Aplikasi Jamsostek Mobile pada Google Play Store Menggunakan Metode Naïve Bayes. Media Informatika, 24(1), 13–21. DOI: https://doi.org/10.37595/mediainfo.v24i1.316 . Fitri, L. A., & Baita, A. (2025). Optimization of Decision Tree Algorithm for Chronic Kidney Disease Classification Based on Particle Swarm Optimization (PSO). Journal of Applied Informatics and Computing, 9(1), 178–186. DOI: https://doi.org/10.30871/jaic.v9i1.8940 . Jupri, M., & Sarno, R. (2018). Taxpayer Compliance Classification using C4.5, SVM, KNN, Naive Bayes and MLP. In 2018 International Conference on Information and Communications Technology (ICOIACT) (pp. 297–303). IEEE. DOI: https://doi.org/10.1109/ICOIACT.2018.8350710 . Butsianto, S., Fauziah, S., Naya, C., & Maulana, F. (2024). Sentiment Analysis of Indosat’s Mobile Operator Services on Twitter using the Naïve Bayes algorithm. Brilliance: Research of Artificial Intelligence, 4(1), 245–254. DOI: https://doi.org/10.47709/brilliance.v4i1.4084 . Firdaus, T. J., Indra, J., Lestari, S. A. P., & Hikmayanti, H. (2024). Sentiment Analysis of the Sambara Application using the Support Vector Machine Algorithm. Jurnal Teknik Informatika (Jutif), 5(4), 1183–1192. DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2673 . Azarine, D., Rahaningsih, N., & Dana, R. D. (2025). Analisis Sentimen terhadap Pengguna Aplikasi Jamsostek Mobile pada Google Play Store Menggunakan Metode Naïve Bayes. Media Informatika, 24(1), 13–21. DOI: https://doi.org/10.37595/mediainfo.v24i1.316 . Dewi, K. K., Kaniawulan, I., & Lestari, C. D. (2023). Analisis Sentimen Pengguna Aplikasi Jamsostek Mobile (JMO) pada Appstore menggunakan metode Naive Bayes. Simtek: Jurnal Sistem Informasi dan Teknik Komputer, 8(2), 333–338. DOI: https://doi.org/10.51876/simtek.v8i2.286 . Putranti, N. D., & Winarko, E. (2014). Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 8(1), 91. DOI: https://doi.org/10.22146/ijccs.3499 . Sulistiowati, Y., & Santoso, B. J. (2025). Analisis Sentimen Ulasan Pengguna Aplikasi Mobile SP4N-LAPOR! dengan Pendekatan Machine Learning. Jurnal Informatika Polinema, 11(3), 283–290. DOI: https://doi.org/10.33795/jip.v11i3.7189 . Junianto, H., Arsi, P., Kusuma, B. A., & Saputra, D. I. S. (2024). Evaluasi Aplikasi Raileo melalui Analisis Sentimen Ulasan Playstore Dengan Metode Naive Bayes. SINTECH (Science and Information Technology) Journal, 7(1), 27–40. DOI: https://doi.org/10.31598/sintechjournal.v7i1.1505 . Mola, S. A. S., Luttu, Y. C., & Rumlaklak, D. N. (2024). Perbandingan Metode Machine Learning dalam Analisis Sentimen Komentar Pengguna Aplikasi Indriver pada Dataset Tidak Seimbang. Jurnal Sistem Informasi Bisnis, 14(3), 247–255. DOI: https://doi.org/10.21456/vol14iss3pp247-255 . Irsyad, H., Farisi, A., & Pribadi, M. R. (2019). Klasifikasi Opini Masyarakat terhadap Jasa ISP MyRepublic dengan Naïve Bayes. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 8(1). DOI: https://doi.org/10.22146/jnteti.v8i1.487 . Prabowo, C. B., Hermanto, T. I., & Ma’ruf, I. (2024). Implementasi Algoritma Support Vector Machine dan Random Forest terhadap Analisis Sentimen Masyarakat dalam Penggunaan aplikasi Tiket.com, Traveloka, dan Agoda pada Google Playstore. Smart Comp: Jurnalnya Orang Pintar Komputer, 13(1). DOI: https://doi.org/10.30591/smartcomp.v13i1.5378 . Wafi, F. A., & Kafa, M. Z. (2025). Determinants of Educated Unemployment in Indonesia: A Comprehensive Logistic Regression Analysis. Convergence: The Journal of Economic Development, 107–126. DOI: https://doi.org/10.33369/convergencejep.v6i2.37353 . Demidova, L., Nikulchev, E., & Sokolova, Y. (2016). The SVM Classifier Based on the Modified Particle Swarm Optimization. International Journal of Advanced Computer Science and Applications, 7(2). DOI: https://doi.org/10.14569/IJACSA.2016.070203 . Sarimole, F. M., & Kudrat, K. (2024). Analisis Sentimen terhadap Aplikasi Satu Sehat pada Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine. Jurnal Sains dan Teknologi, 5(3). DOI: https://doi.org/10.55338/saintek.v5i3.2702 . |
Published
2025-09-30
Section
Articles
How to Cite
Butsianto, S., & Rifa’i, A. M. (2025). Analisis Sentimen Ulasan Aplikasi Jamsostek dengan SVM, Random Forest, dan Logistic Regression. Jurnal Informatika Ekonomi Bisnis, 7(3), 700-706. https://doi.org/10.37034/infeb.v7i3.1266
 This work is licensed under a Creative Commons Attribution 4.0 International License. |
