<![CDATA[The rapid development of digital financial technology in Indonesia has led to increased use of mobile banking applications as the primary medium for customer transactions. Two widely used platforms are BCA Mobile and BRImo, both of which receive various user reviews on the Google Play Store. This study aims to analyze user sentiment toward these applications using the Multinomial Naïve Bayes method and TF–IDF feature weighting. The dataset consists of 18,034 reviews that have undergone text preprocessing, including cleansing, case folding, tokenizing, normalization, filtering, and stemming, as well as lexicon-based sentiment labeling into two classes: positive and negative. The evaluation results show that the developed model achieves an accuracy of 96% for BRImo and 91% for BCA Mobile, demonstrating strong performance in identifying and distinguishing positive and negative reviews. Overall, reviews for BRImo are predominantly positive, while BCA Mobile also shows a positive tendency but receives a higher number of complaints. These findings are expected to provide useful insights for mobile banking developers in improving service quality and enhancing user experience.]]>
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