Sentiment Analysis Of E-Commerce Reviews Using Fine-Tuned Indobert With Class Weights Strategy - Abdan Syakura; Dewi Soyusiawaty (Jurnal Teknik Informatika (Jutif), 2026)
MSMEs in the e-commerce sector face difficulties in converting large volumes of unstructured customer review data into actionable business insights. This challenge is exacerbated by the ambiguity of star ratings, which often do not align with the content of the reviews, making automated sentiment analysis of the text essential. This study implements a systematic sentiment analysis workflow on a case study of 15,278 customer reviews of Toko Pasar Stan Jogja. The method used is fine-tuning a pre-trained Transformer model, namely IndoBERT, which is optimized with class weighting techniques to handle unbalanced datasets. The model's performance was comprehensively evaluated using Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and word cloud visualization metrics. The test results showed that the developed model had very high performance, achieving an overall accuracy of 96.99% and an average F1-Score of 0.97 on the test data. Qualitative analysis also successfully identified that product quality (“fresh”) and logistics efficiency (‘fast’) were the main drivers of satisfaction, while the main complaints centered on the condition of the product upon arrival (“damaged,” “rotten”). This research proves that the optimized Transformer model is not only effective for sentiment classification, but also serves as a strategic tool for extracting concrete business insights.