Analysis of JKN Mobile User Satisfaction using SVM and KNN Methods Through PSO Optimization

Abstract

This study was conducted to evaluate the service quality of the JKN Mobile application developed by the Health Social Security Administering Agency (BPJS Kesehatan) as a means of facilitating participants in accessing health services. Although the application provides convenience for users, there are still various complaints indicating that the service is not running optimally. Therefore, this study aims to analyze the positive and negative sentiments of JKN Mobile application users by comparing the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms based on Particle Swarm Optimization (PSO). The research method was carried out by processing user review data using sentiment classification techniques. The test results showed that the SVM algorithm obtained an accuracy of 85.02% with an AUC value of 0.815, while the PSO-based SVM increased to 86.71% with an AUC of 0.831. The KNN algorithm obtained an accuracy of 39.54% with an AUC of 0.500, while the PSO-based KNN increased to 87.05% with an AUC of 0.736. The results of the study prove that the implementation of PSO is able to improve the accuracy performance of both algorithms.