Optimalisasi Akurasi dan Stabilitas Analisis Sentimen Ulasan E-Commerce Indonesia melalui Fine-Tuning Transformer IndoBERT - Alfina Latifa Maysara; Muljono (Infotekmesin, 2026)
The rapid growth of e-commerce in Indonesia increases the need for sentiment analysis to accurately understand customer perceptions. This study evaluates the effectiveness of the Transformer-based IndoBERT model for sentiment classification on Indonesian e-commerce reviews and compares its performance with four RNN architectures (LSTM, GRU, BiLSTM, and BiGRU). The PRDECT-ID dataset containing 5,400 reviews was processed through preprocessing, an 80:20 data split, RNN training using 5-Fold Cross Validation, and IndoBERT fine-tuning under a hold-out scheme. Unlike previous studies that focused solely on RNN models with a maximum accuracy of 90.7%, this work expands the evaluation by integrating a Transformer-based approach. Results show that IndoBERT achieves 98.52% accuracy and F1-weighted score, outperforming the best RNN models by approximately 0.94–0.95. Paired T-Test and Wilcoxon tests yield p < 0,05, confirming that the performance improvements are statistically significant. IndoBERT demonstrates greater stability and effectiveness for Indonesian sentiment analysis.