<![CDATA[The growth of digital transaction services in Indonesia has driven the increased use of digital wallets such as DANA, resulting in a continuous increase in the number of user reviews. The large number of reviews makes the process of manually reading, sorting, and understanding sentiment trends inefficient and prone to bias. This challenge is exacerbated by the fact that reviews in Indonesian often contain non-standard language, abbreviations, and slang, making it difficult for the system to accurately recognize the context. In addition, the large volume of data also affects the modeling process, where the availability of more data generally improves the model's ability to learn sentiment patterns more stably. To address these issues, this study developed a machine learning-based sentiment classification system capable of automatically processing large numbers of reviews through TF-IDF feature representation. In this study, review data was collected from the Google Play Store, through a cleaning and preprocessing stage before being converted into TF-IDF feature vectors. Four main algorithms were tested, namely Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes, which were then evaluated using accuracy, precision, recall, and F1-score metrics. The test results showed that TF-IDF was able to describe the relationship between words quite well, while the Naive Bayes algorithm provided the most stable performance compared to the other three methods, with an accuracy rate of 79.80%. The model developed can help companies understand user perceptions more quickly and objectively, as well as support data-driven decision making to improve service quality.]]>
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