Sentiment Analysis of Banking Application Reviews on Google Play Store using Support Vector Machine Algorithm

Banking applications are increasingly important in facilitating daily financial transactions. However, to ensure service quality, developers need to understand user feedback. Reviews on the Google Play Store provide important insights related to satisfaction, complaints, and suggestions. Therefore, this study aims to develop the Sentiment Analysis Model for Banking Application Reviews Using Support Vector Machine (SVM). Data collected from three popular banks in Indonesia is used to train and test models. This research also contributes to providing multi -bank dataset which can be a benchmark. Various scenarios of the distribution of training and test data are explored, and repeated tests are carried out with different random state values to get stable results. The results showed that the SVM model was able to achieve good accuracy, with BRI Mobile dataset reaching the highest accuracy of 92.97%, followed by a combined dataset of 90.05%, BCA Mobile 89.73%, and Livin Mandiri 87.46%. Negative reviews are dominated by technical complaints, while positive reviews highlight the ease and reliability of the application. This study shows that the approach used has succeeded in producing competitive performance, and application developers are advised to focus on improving technical aspects, such as fixing login, verification, and transaction problems, in order to increase user satisfaction.