GoPay as one of the digital payment applications in Indonesia faces challenges in understanding user perceptions in the midst of fierce competition. This study aims to develop a user review sentiment analysis model by comparing two approaches to text representation, namely TF-IDF and BERT, as well as two machine learning algorithms, namely Random Forest and Logistic Regression. Review data is obtained from the Google Play Store and processed through pre-processing, feature extraction, and sentiment modeling. The results showed that the combination of BERT + Logistic Regression provided the best performance with an F1 Score of 0.86, showing the superiority of BERT in understanding the semantic context compared to TF-IDF. An important feature analysis identifies financial-related words such as "duitnyaapakah" and "kompensasi" as key issues. This research makes a practical contribution by helping app developers improve the user experience through prioritizing relevant features and solutions to key problems complained of.
