<![CDATA[The increasing number of user reviews in mobile applications is an important source of information in understanding user satisfaction and experience with the services used. One of the applications used in this study is the Qpon application. Reviews left by users often contain positive or negative opinions that can influence other users in making decisions. Therefore, sentiment analysis is needed to determine the tendency of opinions in these reviews. This study aims to classify Qpon application user reviews into two sentiment categories, namely positive and negative. Data were collected through the web scraping method and obtained 866 review data. After going through text preprocessing stages such as removing unimportant words, normalization, and tokenization, the data were analyzed using the TF-IDF method as a feature representation, then classified using the Logistic Regression and Support Vector Machine (SVM) algorithms. The testing process was carried out using the Stratified K-Fold Cross Validation technique and measured based on five evaluation metrics, namely accuracy, precision, recall, F1-score, and ROC AUC. The results showed that SVM had the highest accuracy and precision values, while Logistic Regression was superior in recall and ROC AUC. These findings indicate that SVM is superior in terms of classification accuracy, while Logistic Regression is more sensitive to positive reviews. This study is expected to be used as a reference for the development of a sentiment analysis system to improve application services based on user review data.]]>
