Implementation of the IndoBERT-LSTM Model for Indonesian Sentiment Analysis withan Explainable AI Approach Using SHAP

Abstract

This study aims to develop an Indonesian sentiment analysis model that achieves high classification performance while providing post-hoc explanations of prediction results.The study utilized a quantitative experimental approach using the IndoNLU SmSA dataset, comprising 11,000 training and 1,260 validation samples across positive, neutral, and negative categories. The proposed model integrates an IndoBERT contextual representation generator with a Bidirectional Long Short-Term Memory (BiLSTM) network to model sequential relationships. Furthermore, SHapley Additive exPlanations (SHAP) are applied to provide post-hoc interpretations of the model's predictions by identifying individual token contributions. The IndoBERT-BiLSTM model achieved an accuracy of 92.78%, a Macro F1-score of 0.9013, and a Macro Average AUC of 0.976, outperforming standard LSTM and fine-tuned IndoBERT baselines. However, learning curve analysis indicated mild overfitting during the training process. SHAP visualizations successfully explained the token-level contributions to the classification decisions, providing transparency into the model's reasoning. This study demonstrates the integration of the IndoBERT-BiLSTM architecture with SHAP to deliver accurate and explainable Indonesian sentiment analysis. The approach effectively bridges the gap between deep learning performance and decision transparency without compromising classification accuracy.