KINERJA MULTINOMIAL NAÏVE BAYES PADA ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI MOBILE JKN
DOI:
https://doi.org/10.31949/j-ensitec.v12i01.16656Abstract
The Mobile JKN application is a key digital service provided by BPJS Kesehatan to support access to national health insurance. User reviews on Google Play Store contain rich information about satisfaction and technical issues, but these data are unstructured and difficult to interpret at scale. This study aims to evaluate the performance of a Multinomial Naïve Bayes model for classifying sentiment in Indonesian-language reviews of the Mobile JKN application. Approximately 10,000 recent reviews were collected via web scraping using the google-play-scraper library and processed through several text preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. The sentiment labels (positive, negative, neutral) were automatically derived from the rating scores using a distant supervision approach. Text features were represented using TF–IDF and used to train and test a Multinomial Naïve Bayes classifier. Model performance was evaluated using accuracy, precision, recall, and F1-score, complemented by a confusion matrix, sentiment distribution visualization, and wordclouds for each sentiment class. The results show that the model achieves good overall accuracy and performs particularly well in identifying positive and negative sentiments, while the neutral class remains more challenging due to shorter review length and semantic overlap with the other two classes. Sentiment distribution indicates that negative reviews still dominate, highlighting persistent technical issues such as login failures, verification problems, and application errors. These findings demonstrate that Naïve Bayes combined with TF–IDF is effective for large-scale sentiment analysis of user feedback on public service applications.
Keywords:
sentiment analysis, Naïve Bayes, Mobile JKN, TF–IDF, user reviewsDownloads
References
[1] D. Yuliani, “Analysis of public perception of JKN services using text mining,” Jurnal/Konferensi Tidak Diketahui, 2022.
[2] R. Ramadhani, “User satisfaction analysis of digital health applications based on reviews,” Jurnal/Konferensi Tidak Diketahui, 2023.
[3] E. D. Madyatmadja, B. N. Yahya, and C. Wijaya, “Contextual text analytics framework for citizen report classification: A case study using the Indonesian language,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3158940.
[4] K. J. Tan, “Text preprocessing and algorithm selection for sentiment analysis reliability,” Jurnal/Konferensi Tidak Diketahui, 2023.
[5] N. A. M. Razali et al., “Opinion mining for national security: Techniques, domain applications, challenges and research opportunities,” J Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00536-5.
[6] A. Gasparetto, A. Zangari, M. Marcuzzo, and A. Albarelli, “A survey on text classification: Practical perspectives on the Italian language,” PLoS One, vol. 17, no. 7, 2022, doi: 10.1371/journal.pone.0270904.
[7] Q. Shen, S. Han, Y. Han, and X. Chen, “User review analysis of dating apps based on text mining,” PLoS One, vol. 18, no. 4, 2023, doi: 10.1371/journal.pone.0283896.
[8] M. Hidayatullah, “Sentiment analysis on digital public service reviews using Naïve Bayes,” Jurnal/Konferensi Tidak Diketahui, 2023.
[9] W. Utami and D. Kurniawan, “Evaluation of PeduliLindungi application reviews using Naïve Bayes,” Jurnal/Konferensi Tidak Diketahui, 2022.
[10] S. A. Rizky and L. Sulastri, “Sentiment analysis on digital health service applications,” Jurnal/Konferensi Tidak Diketahui, 2021.
[11] E. S. Lestari and A. Hidayat, “Sentimen pengguna aplikasi e-commerce menggunakan Naïve Bayes,” Jurnal/Konferensi Tidak Diketahui, 2021.
[12] R. Nasrullah and A. Fauzi, “Sentiment analysis in digital public service evaluation,” Jurnal/Konferensi Tidak Diketahui, 2023.
[13] F. Damanik, A. W. Widayanti, and C. Wiedyaningsih, “User acceptance of Mobile-JKN: Insights from the Technology Acceptance Model,” Jurnal Administrasi Kesehatan Indonesia, vol. 12, no. 2, 2024, doi: 10.20473/jaki.v12i2.2024.206-217.
[14] A. W. Wibowo, “The importance of stemming for Indonesian language text analysis,” Jurnal/Konferensi Tidak Diketahui, 2020.
[15] H. Nabilah, P. Permatasari, A. V El-Tsana, R. P. Anggitya, and A. R. ‘Aisy, “The implementation of the JKN mobile application as an effort to improve the quality of health services in Indonesia: Literature review,” Jurnal Kesehatan, vol. 15, no. 3, 2024, doi: 10.35730/jk.v15i3.1181.
[16] A. P. Zulfa, A. Makmun, Z. K. Novriansyah, F. Sommeng, and Dahlia, “The relationship between the use of Mobile JKN application and health services at Majene Regency Hospital,” Journal of Health Policy and Management, vol. 10, no. 2, 2025, doi: 10.26911/thejhpm.s2025.10.02.01.
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