<![CDATA[Digital transformation has driven innovation in banking services, exemplified by the Octo Mobile application launched by PT Bank CIMB Niaga Tbk. User reviews on the Google Play Store serve as a rich source of unstructured data that can be leveraged to measure user satisfaction in real-time. This study aims to analyze user satisfaction sentiment regarding the Octo Mobile application post-rebranding using an optimized Support Vector Machine (SVM) algorithm. The research methodology adopts a text mining approach, employing web scraping techniques to collect 8,000 user reviews from the period of January to December 2023. The dataset exhibited a significant class imbalance (85.56% positive and 14.44% negative). To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was implemented. As a novel approach, this study applied a customized banking colloquial normalization dictionary during the pre-processing stage to handle slang and transactional abbreviations commonly found in reviews. Feature extraction was conducted using Term Frequency-Inverse Document Frequency (TF-IDF), followed by SVM classification with hyperparameter optimization via Grid Search. The test results demonstrated that the SVM model with an RBF kernel (C=10, γ=0.1) achieved an accuracy of 88.18% and an F1-Score of 87.93% for the negative class, successfully surpassing the 85% accuracy target and outperforming previous studies. The final analysis revealed that 85.56% of users expressed satisfaction with the user interface and transaction ease, whereas 14.44% expressed dissatisfaction, predominantly regarding technical issues such as lagging, system errors, and OTP delays. These findings prove that the combination of SMOTE, contextual normalization, and SVM optimization yields a valid and unbiased analysis, providing strategic recommendations for bank management.]]>
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