Naive Bayes Classifier on Twitter Sentiment Analysis BPJS of HEALTH (original) (raw)

2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), 2019

Abstract

Public health insurance is one indicator of the success of the government's active role in managing and facilitating its citizens. Health media and excellent facilities undoubtedly read a positive impact on the development of society, especially at this time. BPJS, as a government health media for the people of Indonesia, of course, must bring change and be a solution to the imbalance of health services for small and medium people. Sentiment analysis of BPJS products is one solution to get information on the active role of the community as the primary user of their health products. Sentiment analysis is carried out by utilizing social media as the primary basis for data collection. In this study, the initial stage taken was data collection and continued to do Post Tagging on community tweet data. Furthermore, these data are classified again using the Naïve Bayes model to obtain optimal results. The results of the study note that BPJS health services get an accuracy rate of 70% negative for payment topics and 72% positive for information topics, and get a 65% score likely from users in using BPJS services as their health service.

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