<![CDATA[The advancement of information technology has significantly impacted various sectors, including digital banking. BRImo, a mobile banking application from Bank Rakyat Indonesia (BRI), has been widely used, generating numerous user reviews that reflect their experiences. This study applies IndoBERT, a transformer-based model specifically designed for the Indonesian language, to analyze sentiment in BRImo user reviews. IndoBERT excels in handling the unique characteristics of the Indonesian language, such as informal and mixed-language usage. The dataset was collected from Kaggle and processed through labeling, data balancing, and splitting into 80% training, 10% validation, and 10% testing data. The IndoBERT model was evaluated using a confusion matrix and achieved 90% accuracy, with F1-scores of 0.89 for negative, 0.91 for neutral, and 0.90 for positive sentiments. Sentiment analysis results indicate that a significant portion of negative reviews highlight issues related to login difficulties, transaction failures, and slow customer service response times. These insights can help BRI enhance application reliability and customer support efficiency. This study demonstrates that IndoBERT is effective in sentiment analysis for Indonesian text and can be utilized to enhance BRImo services by providing deeper insights into user feedback.]]>
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