Analisis Sentimen Terhadap Ulasan Aplikasi Mobile JKN Menggunakan Metode Machine Learning Logistic Regression, SVM, dan CSVM - Fernando, Moch. Firman; Ahmad, Davin Anezta; Rachmanto, Nugroho Fajar; Wara, Shindi Shella May; Hindrayani, Kartika Maulida (Hasanuddin University, 2025)
One of the digital-based public service innovations in the health sector is the Mobile JKN application developed by BPJS Kesehatan. This application allows people to get health services more easily, effectively, and integrated. The purpose of this study is to evaluate user perceptions of the Mobile JKN application through collecting reviews from the Google Play Store. The collected data was analyzed using TF-IDF text mining technique and Chi-Square feature selection. Furthermore, logistic regression, support vector machine (SVM), and clustered SVM (CSVM) algorithms were used to perform sentiment classification. Comments were categorized into three categories: positive, neutral, and negative. The evaluation results show that CSVM has an accuracy value of 93%, precision of 94%, recall of 84%, and F1 value of 89%. Although features such as online registration and digital cards received positive feedback, sentiment analysis showed that most reviews were negative, especially regarding technical issues. The results show that ML-based algorithms can be effectively used to assess how people perceive digital services. These results can be used as a basis for BPJS Kesehatan to improve and develop new services.