Forecasting IHSG Stock Prices Using an Attention-Based CNN-BiGRU Hybrid Deep Learning

This study develops an IHSG stock price forecasting model using a hybrid CNN–BiGRU architecture enhanced by an attention mechanism. The key novelty lies in combining CNN-based local pattern extraction with BiGRU-based bidirectional temporal modeling, while attention selectively emphasizes the most informative time steps, improving representation quality for complex and noisy financial series. Historical IHSG data from public sources were preprocessed through feature engineering and normalization, followed by XGBoost-based feature selection to retain the most predictive variables. Model robustness was assessed in two settings: (i) the full dataset and (ii) a “cleaned” dataset excluding the extreme COVID-19 volatility period. The proposed model achieved strong accuracy, with MAE/RMSE of 0.0125/0.02 on the full dataset and 0.0167/0.03 on the cleaned dataset, while Pearson correlation remained close to 1 in both scenarios, indicating high alignment with actual IHSG movements. A 30-day ahead forecast produced a stable and realistic trend. Overall, the CNN–BiGRU with attention provides an effective and robust approach for capturing multi-scale temporal patterns in IHSG forecasting.