Perbandingan Kinerja Naive Bayes, Support Vector Machine dan Random Forest Untuk Analisis Sentimen Aplikasi Brimo
-
Amelia Darwin
*
Universitas Sriwijaya, Palembang,
Indonesia
- Dinda Lestarini Universitas Sriwijaya, Palembang, Indonesia
- Iin Seprina Universitas Sriwijaya, Palembang, Indonesia (*) Corresponding Author
Abstract
The development of financial technology has driven the increasing use of mobile banking, including BRImo, owned by Bank Rakyat Indonesia (BRI). However, user reviews on the Google Play Store show various complaints such as login difficulties, system errors, and failed transactions. This study aims to analyze BRImo user sentiment using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Data were obtained from 4,996 reviews through web scraping and labeled based on ratings with categories 1-3 negative and 4-5 positive. The labeling process obtained 4,123 positive reviews and 873 negative reviews, which were then balanced using the Synthetic Minority Oversampling Technique (SMOTE). Feature extraction was performed using TF-IDF. Test results showed that Random Forest provided the best performance with an accuracy of 0.87, a recall of 0.70, and an F1-score of 0.65 in the negative class, and an F1-score of 0.92 in the positive class. The macro F1-score reached 0.79, higher than SVM (0.69) and Naive Bayes (0.70). This finding indicates that Random Forest is more effective in classifying BRImo user sentiment, especially after data balancing, and can serve as a reference for developers in improving the quality of application services.
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