Improving Sentiment Classification of Kredit Pintar Reviews Using IndoBERT, SMOTE, and Stacking Ensemble

Abstract

Kredit Pintar is one of the most widely used fintech applications in Indonesia, generating millions of user reviews on the Google Play Store that reflect diverse user experiences. These reviews provide valuable insights into application performance; however, extracting sentiment from such unstructured and imbalanced textual data remains a challenging task. This study aims to improve sentiment classification of Kredit Pintar user reviews by proposing a hybrid approach that integrates IndoBERT, SMOTE (Synthetic Minority Over-Sampling Technique), and a stacking ensemble model. From 2020 to 2024, 2,278 user reviews were classified into positive, neutral, and negative categories based on star ratings. SMOTE was employed to rectify class imbalance, whereas IndoBERT gathered contextual representations of the Indonesian language. Furthermore, a stacking ensemble combining IndoBERT, Random Forest, and SVM (Support Vector Machine) was implemented to enhance classification performance. Experimental results show that IndoBERT without data balancing achieved an accuracy of 84%, whereas the proposed combination of IndoBERT, SMOTE, and stacking ensemble consistently produced superior performance, achieving 92% accuracy, precision, recall, and F1-score. The findings demonstrate that integrating language-specific transformer models with data balancing and ensemble techniques effectively improves sentiment classification. This study contributes to the advancement of Indonesian-language natural language processing in the fintech domain and provides practical insights for fintech developers in understanding user perceptions and improving digital financial services.