Sentiment Analysis of Transjakarta App Reviews Using the Naive Bayes Algorithm

Ahmad Fikri Haikal; Yosep Nuryaman

doi:10.59934/jaiea.v5i1.1677

Authors

Ahmad Fikri Haikal Universitas Bina Sarana Informatika
Yosep Nuryaman Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i1.1677

Keywords:

crisp-dm, naive bayes, sentiment analysis, text mining, transjakarta

Abstract

In utilizing digital technology in the transportation sector, Transjakarta has introduced a mobile-based application to facilitate public mobility. The number of Transjakarta passengers has increased significantly, making it important to know whether users are satisfied with the application or not. This study aims to classify sentiments and identify the aspects and issues that frequently arise in user reviews of the Transjakarta app using the Naive Bayes algorithm. This study employs the CRISP-DM methodology. Analysis was conducted by scraping 1,000 Google Play Store review data based on MOST RELEVANT, following preprocessing, TextBlob data labeling, TF-IDF weighting, and oversampling (SMOTE) methods. The implementation of the Naive Bayes algorithm with an 80:20 resulted in 684 positive data and 315 negative data, yielding a model accuracy of 78%. For the positive sentiment class, the precision was 82%, recall was 85%, and the F1-score was 84%. For the negative sentiment class, the precision was 67%, recall was 62%, and the F1-score was 65%. Based on the visualization, the words that frequently appear in positive reviews are “bagus”, “lengkap”, and “mudah”, and negative reviews are “ribet”, “susah”, and “error”.

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