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All Journal DINAMIKA DOTCOM TEKNOLOGI INFORMASI SMATIKA Syntax Literate: Jurnal Ilmiah Indonesia International Journal of Science and Environment Transaction on Informatics and Data Science Mahardika Adiwidia Communica : Journal of Communication
Mochamad Husni
STMIK PPKIA Pradnya Paramita Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Journal : Transaction on Informatics and Data Science

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Comparison of the Accuracy Between Naive Bayes Classifier and Support Vector Machine Algorithms for Sentiment Analysis in Mobile JKN Application Reviews Septiani, Erni; Akhriza, Tubagus M.; Husni, Mochamad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12232

Abstract

The Mobile JKN (National Health Insurance) application is a form of BPJS Health's commitment to implementing health insurance programs since 2014. The large number of reviews of the Mobile JKN application on the Google Play Store requires sentiment analysis with an algorithm that produces the best accuracy. This research compares the accuracy obtained from the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. This algorithm is implemented directly in sentiment analysis and combined with the Synthetic Minority Over-Sampling Technique (SMOTE) technique to overcome data imbalance. The data in this research was obtained from reviews of the Mobile JKN application on the Google Play Store using the data scraping method. We use data scraping and labeling processes before performing sentiment analysis. The sentiment analysis process includes text preprocessing and processing (modeling) by dividing the data into 30%, 40%, and 50% test data, with the rest becoming training data. The results of this research showed that the algorithm with the best accuracy was the NBC algorithm using the SMOTE technique with 50% test data and the SVM algorithm without the SMOTE technique with 50% test data. Both give the same accurate results, namely 0.90 or 90%. Experiments show that the amount of test data and the application of SMOTE affect the accuracy of the two compared algorithms.
Co-Authors Achmad Saiful Rizal Akhriza, Tubagus M. Andre Kurniawan Dwi Safiroh Utsalina Lestari, Elvira Verdhi Mahmud Yunus Muhammad Miqdad Mufadhdhal Patrissia, Ressa Uli Putranto, Algooth Sa’atul Ihsan Saipi -, Saipi Septiani, Erni Uli Patrissia, Ressa