Analisis Sentimen Pada Ulasan Aplikasi Bank Syariah Indonesia Mobile Menggunakan Support Vector Machine dan Naïve Bayes - Aqilla, Nabila Fadia; Jazman, Muhammad; Syaifullah, Syaifullah; Rahmawita, Medyantiwi (Building of Informatics, Technology and Science (BITS), 2025)
The internet plays a crucial role in facilitating various human activities, including in the field of electronic banking services, which encompasses various financial services such as ATMs, internet banking, SMS banking, and mobile banking. All of these aim to enhance service quality with a focus on security, convenience, and effectiveness. BSI is one of the banks offering mobile banking services. Based on user reviews, the BSI Mobile app often experiences technical issues such as bugs and transaction failures. To assess the level of satisfaction with the app, the researcher uses sentiment analysis methods. This method also helps potential customers identify aspects that need improvement or development in the products and services to enhance their quality. The study employs Support Vector Machine (SVM) and Naïve Bayes algorithms. The test results show that the Naïve Bayes algorithm achieves an accuracy of 74.37%, recall of 74.37%, precision of 75.46%, and an F1-score of 74.5%. Meanwhile, the SVM algorithm achieves an accuracy of 77.39%, precision of 77.8%, recall of 77.39%, and an F1-score of 77.38%. These findings indicate that SVM performs better in sentiment classification tasks compared to Naïve Bayes. With its superior performance, SVM is the more suitable algorithm for analyzing user perceptions of the BSI Mobile app. Therefore, the findings of this study can contribute to the development of more innovative digital service strategies and enhance competitiveness in the digital era.