Sentiment Analysis of Public Reviews of the Brompit Application Using Naive Bayes and SVM Algorithms
Analisis Sentimen Ulasan Masyarakat Aplikasi Brompit Menggunakan Algoritma Naive Bayes dan SVM
DOI:
https://doi.org/10.21070/ups.9887Keywords:
Sentiment, BromPit, Naive Bayes, SMOTE, SVMAbstract
BromPit application was developed to facilitate Honda motorcycle owners in booking service appointments, tracking vehicle status, and locating authorized service centers; however, user reviews on the Google Play Store indicate varied responses. This study focuses on sentiment analysis of BromPit application usage and compares the effectiveness of the Naive Bayes and Support Vector Machine methods in classifying textual data. The research procedure includes review data collection and labeling, preprocessing, training and testing data splitting, TF-IDF weighting, the application of SMOTE, and accuracy evaluation. The results show that positive reviews dominate with 7,157 entries, followed by 1,351 negative and 221 neutral reviews. Model testing indicates that Naive Bayes achieves an accuracy of 82.59%–83.05%, while SVM attains 81.90%–82.51%, indicating that Naive Bayes is more effective
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