Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm

Abstract

The rapid development of digital public services requires a deeper understanding of user perceptions and experiences regarding government applications, including Cek Bansos. This study aims to identify the polarity of user reviews by applying the Multinomial Naïve Bayes algorithm to review data collected from the Google Play Store. The methodology includes text preprocessing, sentiment labeling, feature extraction using TF–IDF, and model training and evaluation based on accuracy, precision, recall, and F1-score. The results show that the model achieves an accuracy of 79.5%, with very high performance in the negative class (recall 0.97) but poor performance in the neutral class due to data imbalance. The dominance of negative sentiment in the dataset indicates that users face significant technical difficulties, particularly in registration, verification, and service access. These findings demonstrate that Multinomial Naïve Bayes is effective as a baseline model for sentiment analysis; however, improving data balance and quality is necessary to produce a more stable, accurate, and representative model for evaluating digital public services.