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All Journal Jurnal Riset Informatika
Alvie Delia Cahyani
STMIK Nusa Mandiri
Computer Science & IT

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Sentiment Analysis of Digital Wallet Service Users Using Naïve Bayes Classifier and Particle Swarm Optimization Alvie Delia Cahyani; Tati Mardiana; Laela Kurniawati
Jurnal Riset Informatika Vol. 2 No. 4 (2020): Period September 2020
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1091.925 KB) | DOI: 10.34288/jri.v2i4.114

Abstract

Digital wallet services adequately provide many benefits to their users. However, not all users of digital wallet services have a favourable opinion of the service. Therefore, online transportation service companies need to carry out an analysis to determine general sentiment towards their products. The Naïve Bayes Classifier method represents a simple, fast method with excellent accuracy and performs comparatively well for classifying data. However, the Naïve Bayes Classifier method assumes that the attributes are independent, so it can cause the accuracy to be less than optimal. This study aims to improve the accuracy of the Naive Bayes classification for classifying public opinion on digital wallet services using Particle Swarm Optimization. This study manages data from Twitter as much as 490 tweet data. The test results with the confusion matrix and ROC curves show an increase in the accuracy of the Naïve Bayes Classifier method for the Dana digital wallet from 60.00% to 91.67% and the iSaku digital wallet from 53.23% to 85.00%. The T-test and ANOVA test results show that the test results of both classification methods provide significant differences in the accuracy value.
Co-Authors Laela Kurniawati Tati Mardiana