Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM, KNN, and Naive Bayes for CRM Enhancement - Tri Zafira, Zahra; Ditha Tania, Ken; Kurnia Sari, Winda (Politeknik Negeri Batam, 2025)
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