<![CDATA[Mobile banking applications generate large volumes of user reviews that can be used to evaluate digital banking service quality. This study analyzes sentiment in Indonesian mobile banking reviews on the Google Play Store in 2025 and compares the performance of Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). A total of 270,977 reviews were collected from five applications, namely BRImo, Livin’ by Mandiri, myBCA, BYOND by BSI, and wondr by BNI. The data were processed through text cleaning, case folding, tokenization, word normalization, stopword removal, and negation retention. Sentiment labels were generated automatically using the InSet Lexicon and validated on 450 stratified review samples. The validation results showed moderate agreement between automatic and manual labels, with an accuracy of 0.6111, macro-F1 score of 0.5384, and Cohen’s Kappa of 0.4167. Review texts were represented using TF-IDF with unigram and bigram features, and model performance was evaluated across six stratified data-splitting scenarios using accuracy, macro precision, macro recall, macro-F1, confusion matrix, and macro ROC-AUC. The results show that RF in Scenario 6 achieved the best performance, with an accuracy of 0.9524, macro-F1 score of 0.8498, and macro ROC-AUC of 0.9798. Dominant-term analysis indicates recurring complaints related to login failures, transaction errors, system disruptions, and application updates. This study contributes a cross-application comparison of machine learning algorithms for Indonesian mobile banking sentiment analysis and provides practical insights for improving service reliability. However, the findings should be interpreted within the limitation of automatic lexicon-based labeling, particularly for neutral and context-dependent reviews. ]]>
