Sentiment analysis of mobile jkn application reviews using the multinomial naïve bayes algorithm
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Published:
May 30, 2025
Keywords:
Analisis Sentimen, Google Play Store, Mobile JKN, Multinomial Naive Bayes
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Computer Science
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Abstract
Digital transformation in healthcare services has significantly improved public access to information. The Mobile JKN (National Health Insurance) application was developed to facilitate easier access to healthcare services. However, its effectiveness needs to be evaluated through sentiment analysis of user reviews on the Google Play Store. This study aims to determine user sentiment toward the Mobile JKN application using the Multinomial Naïve Bayes method, a commonly used classification technique in machine learning. The data was collected through web scraping and processed through several stages, including tokenization, stopword removal, and text normalization. Sentiment labels were then assigned using a lexicon-based approach, specifically the INSET lexicon, before classification. The analysis revealed that the majority of reviews expressed negative sentiment, particularly concerning application performance, technical issues, and healthcare service quality. The results also showed that the Multinomial Naïve Bayes model was able to classify the data with an accuracy of 81%. Therefore, the Mobile JKN application still requires technical improvements and service enhancements to provide a better user experience. This study offers valuable insights for developers and can serve as a foundation for policy-making to improve the quality of digital healthcare services
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How to Cite
Paratama, I. P. D. E. ., Bagus Ariana, A. A. G. ., Labasariyani, N. L. P. . and Rahayu G, N. L. W. S. (2025) “Sentiment analysis of mobile jkn application reviews using the multinomial naïve bayes algorithm”, Jurnal Mantik, 9(1), pp. 101-110. doi: 10.35335/mantik.v8i5.6221.
References
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Dan, C. C., & Saufa Yardha, L. (2024). Analisis Sentimen Pengguna Aplikasi Mobile Jkn Menggunakan Algoritma Naïve Bayes. Journal of Science and Social Research, 4307(2), 555–563. http://jurnal.goretanpena.com/index.php/JSSR
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Fahmy Amin, M. (2023). Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 7(1), 0–0. https://doi.org/10.21608/erjeng.2023.296718
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Kusumo, S. (2022). Penerapan Web Scraping Deskripsi Produk Menggunakan Selenium Python Dan Framework Laravel. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(4), 3426–3435. https://doi.org/10.35957/jatisi.v9i4.2727
Mantik, J., Saputri, Y. R., & Februariyanti, H. (2022). Sentiment Analysis on Shopee E-Commerce Using the Naïve Bayes Classifier Algorithm. Jurnal Mantik, 6(2), 1349–1357.
Mantik, J., Wanti Wulan Sari, N., Mirwansyah, D., Leonard Tandi, I., & Hakim Hadi Sopyani, A. (2022). Prediction of Students Drop Out With Naïve Bayes. Jurnal Mantik, 6(3), 2685–4236.
Mantiri, J., Wangkai, A., Pongantung, R. J., & Tarore, S. (2023). Tingkat Kepuasan Pelayanan Kesehatan Pada Aplikasi Mobile JKN di Kota Tomohon. Dinamika?: Jurnal Ilmiah Ilmu Administrasi Negara, 11(1), 211–219. https://jurnal.unigal.ac.id/dinamika/article/view/13910
Mauludin Rohman, M., & Adinugroho, S. (2021). Analisis Sentimen pada Ulasan Aplikasi Mobile JKN Menggunakan Metode Maximum Entropy dan Seleksi Fitur Gini Index Text. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(6), 2646–2654. http://j-ptiik.ub.ac.id
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Nurzaman, N., Suarna, N., & Prihartono, W. (2024). Analisis Sentimen Ulasan Aplikasi Threads Di Google Playstore Menggunakan Algoritma Naïve Bayes. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 967–974. https://doi.org/10.36040/jati.v8i1.8708
Priyanto, A., & Ma’arif, M. R. (2018). Implementasi Web Scrapping dan Text Mining untuk Akuisisi dan Kategorisasi Informasi dari Internet (Studi Kasus: Tutorial Hidroponik). Indonesian Journal of Information Systems, 1(1), 25–33. https://doi.org/10.24002/ijis.v1i1.1664
Raffi, M., Suharso, A., & Maulana, I. (2023). Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes Sentiment Analysis of Binar Application Reviews on Google Play Store Using Naïve Bayes Algorithm. Journal of Information Technology and Computer Science (INTECOMS), 6(1), 1–7.
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Roiqoh, S., Zaman, B., & Kartono, K. (2023). Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes. Jurnal Media Informatika Budidarma, 7(3), 1582. https://doi.org/10.30865/mib.v7i3.6194
Saad, S., & Saberi, B. (2017). Sentiment Analysis or Opinion Mining: A Review. International Journal on Advanced Science, Engineering and Information Technology, 7(5), 1660. https://doi.org/10.18517/ijaseit.7.4.2137
Samudera, B. D., Al, H., & Aidilof, K. (2024). Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm. 4(4), 101–112.
Yuyun, Nurul Hidayah, & Supriadi Sahibu. (2021). Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 820–826. https://doi.org/10.29207/resti.v5i4.3146
Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2023). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. IEEE Transactions on Knowledge and Data Engineering, 35(11), 11019–11038. https://doi.org/10.1109/TKDE.2022.3230975
Zulfikar, W. B., Atmadja, A. R., & Pratama, S. F. (2023). Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes. Scientific Journal of Informatics, 10(1), 25–34. https://doi.org/10.15294/sji.v10i1.39952
Bunga, M. T. ., S, B., Djahi, & Nabuasa, Y. Y. (2018). Multinomial Naive Bayes Untuk Klasifikasi Status Kredit Mitra Binaan Di Pt . Angkasa Pura I Program Kemitraan. J-Icon, 6(2), 30–34. https://media.neliti.com/media/publications/292504-multinomial-naive-bayes-untuk-klasifikas-2536567f.pdf
Dan, C. C., & Saufa Yardha, L. (2024). Analisis Sentimen Pengguna Aplikasi Mobile Jkn Menggunakan Algoritma Naïve Bayes. Journal of Science and Social Research, 4307(2), 555–563. http://jurnal.goretanpena.com/index.php/JSSR
Evans, R. (2022). Confusion Matrices and Accuracy Statistics for Binary Classifiers Using Unlabeled Data: The Diagnostic Test Approach. 1980, 1–10. http://arxiv.org/abs/2208.12664
Fahmy Amin, M. (2023). Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 7(1), 0–0. https://doi.org/10.21608/erjeng.2023.296718
Fibriyanti Arminda, N., Sulistiyowati, N., & Nur Padilah, T. (2023). Implementasi Algoritma Multinomial Naive Bayes Pada Analisis Sentimen Terhadap Ulasan Pengguna Aplikasi Brimo. JATI (Jurnal Mahasiswa Teknik Informatika), 7(3), 1817–1822. https://doi.org/10.36040/jati.v7i3.7012
Herwijayanti, B., Ratnawati, D. E., & Muflikhah, L. (2018). Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(1), 306–312. http://j-ptiik.ub.ac.id
Jeffson Sagala, G., & Samuel, Y. T. (2024). Sentiment Analysis on ChatGPT App Reviews on Google Play Store Using Random Forest Algorithm, Support Vector Machine and Naïve Bayes. International Journal of Engineering Business and Social Science, 2(04), 1194–1204. https://doi.org/10.58451/ijebss.v2i04.148
Khalid, N., Abdul-Rahman, S., Wibowo, W., Abdullah, N. A. S., & Mutalib, S. (2023). Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic. International Journal of Advances in Intelligent Informatics, 9(3), 457–471. https://doi.org/10.26555/ijain.v9i3.1367
Kusumo, S. (2022). Penerapan Web Scraping Deskripsi Produk Menggunakan Selenium Python Dan Framework Laravel. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(4), 3426–3435. https://doi.org/10.35957/jatisi.v9i4.2727
Mantik, J., Saputri, Y. R., & Februariyanti, H. (2022). Sentiment Analysis on Shopee E-Commerce Using the Naïve Bayes Classifier Algorithm. Jurnal Mantik, 6(2), 1349–1357.
Mantik, J., Wanti Wulan Sari, N., Mirwansyah, D., Leonard Tandi, I., & Hakim Hadi Sopyani, A. (2022). Prediction of Students Drop Out With Naïve Bayes. Jurnal Mantik, 6(3), 2685–4236.
Mantiri, J., Wangkai, A., Pongantung, R. J., & Tarore, S. (2023). Tingkat Kepuasan Pelayanan Kesehatan Pada Aplikasi Mobile JKN di Kota Tomohon. Dinamika?: Jurnal Ilmiah Ilmu Administrasi Negara, 11(1), 211–219. https://jurnal.unigal.ac.id/dinamika/article/view/13910
Mauludin Rohman, M., & Adinugroho, S. (2021). Analisis Sentimen pada Ulasan Aplikasi Mobile JKN Menggunakan Metode Maximum Entropy dan Seleksi Fitur Gini Index Text. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(6), 2646–2654. http://j-ptiik.ub.ac.id
Muhammad Fernanda Naufal Fathoni, Eva Yulia Puspaningrum, & Andreas Nugroho Sihananto. (2024). Perbandingan Performa Labeling Lexicon InSet dan VADER pada Analisa Sentimen Rohingya di Aplikasi X dengan SVM. Modem?: Jurnal Informatika Dan Sains Teknologi., 2(3), 62–76. https://doi.org/10.62951/modem.v2i3.112
Narmansyah, S., Indar, Rahmadani, S., Arifin, M. A., & M Thaha, R. (2022). Analisis Pemanfaatan Sistem Informasi JKN Mobile Di Kota Makassar. Sehat Rakyat: Jurnal Kesehatan Masyarakat, 1(3), 196–204. https://doi.org/10.54259/sehatrakyat.v1i3.1082
Nurzaman, N., Suarna, N., & Prihartono, W. (2024). Analisis Sentimen Ulasan Aplikasi Threads Di Google Playstore Menggunakan Algoritma Naïve Bayes. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 967–974. https://doi.org/10.36040/jati.v8i1.8708
Priyanto, A., & Ma’arif, M. R. (2018). Implementasi Web Scrapping dan Text Mining untuk Akuisisi dan Kategorisasi Informasi dari Internet (Studi Kasus: Tutorial Hidroponik). Indonesian Journal of Information Systems, 1(1), 25–33. https://doi.org/10.24002/ijis.v1i1.1664
Raffi, M., Suharso, A., & Maulana, I. (2023). Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes Sentiment Analysis of Binar Application Reviews on Google Play Store Using Naïve Bayes Algorithm. Journal of Information Technology and Computer Science (INTECOMS), 6(1), 1–7.
Rifaldi, D., Fadlil, A., & Herman. (2024). Implementation of Word Trends Using a Machine Learning Approach with TF-IDF and Latent Dirichlet Allocation. International Journal on Informatics Visualization, 8(4), 2297–2304. https://doi.org/10.62527/joiv.8.4.2452
Roiqoh, S., Zaman, B., & Kartono, K. (2023). Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes. Jurnal Media Informatika Budidarma, 7(3), 1582. https://doi.org/10.30865/mib.v7i3.6194
Saad, S., & Saberi, B. (2017). Sentiment Analysis or Opinion Mining: A Review. International Journal on Advanced Science, Engineering and Information Technology, 7(5), 1660. https://doi.org/10.18517/ijaseit.7.4.2137
Samudera, B. D., Al, H., & Aidilof, K. (2024). Sentiment Analysis of User Reviews on BSI Mobile and Action Mobile Applications on the Google Play Store Using Multinomial Naive Bayes Algorithm. 4(4), 101–112.
Yuyun, Nurul Hidayah, & Supriadi Sahibu. (2021). Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 820–826. https://doi.org/10.29207/resti.v5i4.3146
Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2023). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. IEEE Transactions on Knowledge and Data Engineering, 35(11), 11019–11038. https://doi.org/10.1109/TKDE.2022.3230975
Zulfikar, W. B., Atmadja, A. R., & Pratama, S. F. (2023). Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes. Scientific Journal of Informatics, 10(1), 25–34. https://doi.org/10.15294/sji.v10i1.39952
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