<![CDATA[The rapid growth of digital wallet users in Indonesia, reaching millions of active users, has generated a massive volume of reviews on the Google Play Store. This textual data contains crucial insights regarding customer satisfaction but is often underutilized due to challenges in processing unstructured data. This study aims to perform a comparative performance analysis between the probabilistic Naive Bayes algorithm and the distance-based K-Nearest Neighbor (KNN) in classifying user sentiment for DANA, OVO, DOKU, and LinkAja applications. This study utilizes a dataset of 18,869 reviews which exhibits a mild class imbalance with a negative sentiment dominance of 57.54%. To preserve the representation of the large original data, this research applies Stratified Sampling without synthetic data balancing techniques (such as SMOTE), followed by comprehensive preprocessing stages aided by the Sastrawi library and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Model optimization was systematically conducted using GridSearchCV for Naive Bayes and the Elbow Method to determine the optimal k value for KNN. Empirical test results show that the Naive Bayes algorithm with a smoothing parameter alpha of 0.1 achieved the best performance with an accuracy of 88.5% and an AUC of 0.9237, outperforming KNN at k=27 which obtained an accuracy of 87.4%. The validity of this performance difference was confirmed to be significant through the McNemar statistical test with a p-value of 0.0045. Another crucial finding is computational efficiency, where Naive Bayes proved to be 129 times faster in the prediction process compared to KNN. Based on the significant advantages in accuracy and time efficiency, Naive Bayes is recommended as the superior method for real-time sentiment analysis in the financial technology ecosystem.]]>
