Analysis of user satisfaction with the seabank app using support vector machines (SVM) - Eko Yulianto; Nanang Nuryadi; Taufik Asra (Institute of Computer Science (IOCS), 2026)
The development of financial technology (fintech) in Indonesia has driven the rapid growth of digital banks, one of which is SeaBank. User satisfaction with the SeaBank app is a crucial indicator for maintaining customer loyalty amid intense competition. This study aims to analyze user satisfaction with the SeaBank app based on reviews on the Google Play Store using the Support Vector Machine (SVM) method. Review data was classified into positive sentiment (satisfied) and negative sentiment (dissatisfied). The research stages included data collection, rating-based labeling, text preprocessing (including normalization of banking colloquial language), feature extraction using TF-IDF, handling imbalanced data with SMOTE, and classification using an RBF Kernel SVM. The results show that the application of SMOTE successfully addressed class imbalance, and SVM hyperparameter optimization yielded an accuracy of 89.14% with an F1-Score of 88.75%. The analysis findings indicate that the majority of users are satisfied with the convenience of free transactions, but there are significant complaints regarding OTP delays and errors in the login system.