Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM, KNN, and Naive Bayes for CRM Enhancement
DOI:
https://doi.org/10.30871/jaic.v9i5.10323Keywords:
Sentiment Analysis, Knowledge Discovery, User Loyalty, Machine Learning, Wondr by BNIAbstract
The rapid development of digital banking services has necessitated a deeper understanding of user perceptions and satisfaction levels. This study analyzes sentiment from user reviews of the Wondr by BNI app using a Knowledge Discovery approach and machine learning methods. Three classification algorithms were compared: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes, evaluated with accuracy, precision, recall, and f1-score. The results show that SVM and Naive Bayes achieved the best performance with F1-scores of 0.88 and 0.87, while KNN lagged behind with 0.77. An ANOVA test further confirmed that the performance differences were statistically significant (p < 0.05), with SVM and Naive Bayes consistently outperforming KNN. Word Cloud analysis revealed dominant positive terms such as "easy," "fast," and "transaction," alongside negative terms like "login," "difficult," and "verification." These findings highlight user appreciation for simplicity and speed, while pointing out functional issues that require attention. This research not only enriches the literature on Indonesian-language sentiment analysis in the financial sector but also provides practical insights for Customer Relationship Management (CRM), particularly in strengthening customer retention strategies and guiding UX redesign for digital banking services.
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