Article Sidebar

Submitted
29 April 2025
Accepted
24 December 2025
Published
30 December 2025
Download
PDF
Statistic
Read Counter : 36 Download : 22

Main Article Content

Abstract

Public service institutions face constant expectations to provide excellent service to participants and minimize complaints. With advances in technology, participants can now provide direct feedback on public services via online platforms, such as Google Reviews. This study aims to analyze participants’ sentiment toward the services of the BPJS Kesehatan Kupang Branch using a quantitative approach. The data collection process employed Python web scraping to retrieve 530 reviews through March 2025. The collected text underwent comprehensive preprocessing, including cleaning, tokenization, stopword removal, and stemming, to ensure data . We assigned sentiment labels based on star ratings: 4- and 5-star reviews were classified as positive, 1- and 2-star reviews as negative, and 3-star reviews were excluded as neutral. A final dataset of 529 reviews was then processed using the Naïve Bayes classifier. The results show that the Naïve Bayes algorithm successfully classified sentiments with an accuracy rate of 98.11%. Additionally, the analysis revealed that positive sentiment accounted for 98.3%, driven by keywords related to service speed and staff friendliness. These findings indicate that sentiment analysis of online reviews is an effective and objective tool for evaluating participants’ perceptions of public service quality.

Keywords

Review National Health Insurance Google Review Naïve Bayes

Article Details

Creative Commons License

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

How to Cite
Bau, A., & Kapitan, G. D. (2025). Sentiment Analysis of National Health Insurance Participants’ Reviews on Google Reviews. Jurnal Jaminan Kesehatan Nasional, 5(2), 365–378. https://doi.org/10.53756/jjkn.v5i2.344
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Automating Large-scale Health Care Service Feedback Analysis. (2022). Journal of Medical Internet Research Informatics.
  2. Basiri, M. E., et al. (2021). A survey of sentiment analysis and emotion detection in social media. Artificial Intelligence Review.
  3. BPJS Kesehatan RI. (2025). BPJS Kesehatan Profile. Retrieved from https://www.bpjs-kesehatan.go.id/#/profil?tab=visi-dan-misi
  4. BPJS Kesehatan RI. (2024). Participant Satisfaction Survey Results of Regional Deputy XI in 2024.
  5. Chairunnisa, R., Indriati, I., & Adikara, P. P. (2022). Sentiment Analysis on Laid-Off Employees on Twitter Using N-Gram Features and Augmented TF-IDF Probability Weighting with K-Nearest Neighbour. Journal of Information Technology and Computer Science Development, 6(4), 1960–1965.
  6. Cui, J., Wang, Z., Ho, S.-B., & Cambria, E. (2023). Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review, 56, 8469–8510. https://link.springer.com/article/10.1007/s10462-022-10386-z
  7. Handayanto, R. T., Herlawati, H., Atika, P. D., Khasanah, F. N., Yusuf, A. Y., & Septia, D. Y. (2021). Sentiment Analysis on Google Review Site Using Naïve Bayes and Support Vector Machine. Komtika Journal (Computing and Informatics), 5(2), 153–163.
  8. Hudaya, F. (2022). Sentiment Analysis on the Sapawarga-Jabar Super Apps Application Reviews on Google Play Store (bachelor’s thesis, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta).
  9. Jain, P. K., & Pamula, R. (2020). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. arXiv preprint.
  10. Khan, A., et al. (2020). Text classification algorithms for sentiment analysis: A survey. Information Processing & Management, 57(1).
  11. Kowalski, R., Esteve, M., & Mikhaylov, S. J. (2017). Application of natural language processing to determine user satisfaction in public services. Reprint.
  12. Kruse CS, Smith B, Vanderlinden H, Nealand A. Security Techniques for the Electronic Health Records. J Med Syst. 2017 Aug;41(8):127. doi: 10.1007/s10916-017-0778-4. Epub 2017 Jul 21. PMID: 28733949; PMCID: PMC5522514.
  13. Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
  14. Nurfebia, K., & Sriani, S. (2024). Sentiment Analysis of Skincare Products Using the Naive Bayes Method. Journal of Information Systems and Informatics, 6(3), 1663–1676. https://doi.org/10.51519/journalisi.v6i3.817
  15. Osório, L., & Fachada, N. (2024). Patient-centered healthcare: A framework for analyzing patient feedback through sentiment analysis and topic modeling. IFIP AICT.
  16. Rahman, I. F., Hasanah, A. N., & Heryana, N. (2024). Sentiment Analysis of Users’ Reviews on the Samsat Digital National Application (Signal) Using the Naïve Bayes Classifier Method. Journal of Informatics and Applied Electrical Engineering, 12(2).
  17. Saragih, R., & Nugroho, Y. (2023). Sentiment Analysis on E-Commerce Reviews Using Naïve Bayes and Support Vector Machine. Journal of Information Technology and Computer Science (JTIIK), 10(1), 100–107.
  18. Sari, D. P., & Wardhani, A. P. (2022). Evaluation of Public Service Sentiment Analysis Using Machine Learning Algorithms. Journal of Information Technology and Computer Science Development, 6(7), 2780–2787.
  19. Sentiment analysis and visualization of reviews for healthcare service ... (2021). Applied Computational Physics. AIP Publishing.
  20. Utami, H. (2022). Sentiment Analysis of Shopee Indonesia Application Using the Recurrent Neural Network Method. Indonesian Journal of Applied Statistics, 5(1), 31. https://doi.org/10.13057/ijas.v5i1.56825
  21. Villanueva-Miranda, I., Xie, Y., & Xiao, G. (2025). Sentiment analysis in public health: A systematic review of the current state, challenges, and future directions. Frontiers in Public Health.
  22. Wijaya, D., Saputra, R. A., & Irwiensyah, F. (2024). Sentiment Analysis of the Samsat Digital National Application Reviews on Google Play Store Using the Naïve Bayes Algorithm. KLIK: Scientific Review of Informatics and Computer Science, 4(4), 2369–2380.
  23. Xu, X., Xue, Z., Zhang, C., Medri, J., Xiong, J., Zhou, J., Jin, J., Zhang, Y., Ma, S., & Li, L. (2025). Patients Speak, AI Listens: LLM-based analysis of online reviews uncovers key drivers for urgent care satisfaction. arXiv preprint.
  24. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.