Comparative Study of BiLSTM and GRU for Sentiment Analysis on Indonesian E-Commerce Product Reviews Using Deep Sequential Modeling - Nasution, Khairunnisa; Saddami, Khairun; Roslidar, Roslidar; Akhyar, Akhyar; Fathurrahman, Fathurrahman; Aulia, Niza (Informatika, Universitas Jenderal Soedirman, 2025)
Sentiment analysis plays a crucial role in understanding customer perspectives, especially within Indonesian e-commerce platforms. Despite the success of deep learning in high-resource languages, its application to Indonesian sentiment data remains underexplored. Previous studies using models like BERT-CNN or fine-tuned IndoBERT achieved modest results, highlighting the need for more effective architectures for Indonesian language. This study aims to investigate the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) models in classifying buyers’ sentiment from Indonesian product reviews on the PREDECT-ID dataset comprising 5,400 annotated product reviews. Standard NLP preprocessing techniques—including text normalization, tokenization, stopword removal, and stemming—were applied. Both models were trained using Adam and Stochastic Gradient Descent (SGD) optimizers, and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. The GRU model trained with SGD achieved the highest performance, with an accuracy of 94.07%, precision of 93.84%, recall of 94.53%, and F1-score of 94.18%. Notably, the BiLSTM model combined with SGD resulted in competitive results, achieving 93.61% accuracy and 93.84% F1-score. The results confirm that GRU with SGD optimizer, are highly effective for sentiment classification in Indonesian language datasets. By leveraging deep sequential modeling for a low-resource language, this study contributes to the advancement of scalable sentiment analysis systems in underrepresented linguistic domains. The results contribute to the advancement of NLP systems for Indonesian by providing a benchmark for the future development of sentiment analysis tools in low-resource languages.