<![CDATA[The DANA application is a digital wallet service widely used by the public to support various digital financial transaction activities. The high number of users results in numerous reviews on the Google Play Store containing various responses, experiences, and opinions regarding the quality of the DANA application service. However, the large number of review data makes the manual process of identifying and grouping opinions less effective and takes a relatively long time. This study aims to analyze and classify the sentiment of DANA application user reviews on the Google Play Store into positive, negative, and neutral categories and to determine the performance of the algorithm used in the classification process. The solution implemented is sentiment analysis using a text mining approach and Natural Language Processing (NLP) to process user reviews automatically. The research data was obtained through a scraping process and resulted in 3,509 DANA application user reviews. The data then went through preprocessing stages including cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. Then, sentiment labeling and word weighting were carried out using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The data was then divided into 80% training data and 20% testing data. Classification was then performed using the Multinomial Naïve Bayes algorithm. Model performance was evaluated using a Confusion Matrix with Accuracy, Precision, Recall, and F1-Score metrics. The results showed that the Naïve Bayes model produced an Accuracy value of 80.48%, Precision of 76.75%, Recall of 80.48%, and F1-Score of 78.13%. These results indicate that the combination of the TF-IDF method and the Naïve Bayes algorithm is capable of classifying the sentiment of DANA app user reviews with quite good performance and can be used to help obtain an overview of user perceptions of the DANA app based on reviews provided on the Google Play Store]]>
