Sentiment analysis of mobile jkn application reviews using the multinomial naïve bayes algorithm - Paratama, I Putu Dedy Eka; Bagus Ariana, Anak Agung Gede; Labasariyani, Ni Luh Putu; Rahayu G, Ni Luh Wiwik Sri (Institute of Computer Science (IOCS), 2025)
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