Sentiment Analysis of Tokopedia Customer Reviews using IndoBERT and SMOTE for Class Imbalance Handling - Imam Saputra; Mesran Mesran; Guidio Leonarde Ginting (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Sentiment analysis in the Indonesian e-commerce sector faces significant challenges due to the informal nature of language and severe class imbalance, where neutral reviews are often underrepresented. This research proposes a hybrid framework combining the deep semantic capabilities of IndoBERT with the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification fairness. Using a dataset of Tokopedia customer reviews, this study compares a baseline model against a balanced model using SMOTE on 768-dimensional IndoBERT features. The experimental results reveal that while the baseline model achieved a high overall accuracy of 83%, it suffered from an