Comparative Analysis of CNN, LSTM, and CNN–LSTM for Indonesian Stock Prediction - Joddy, Setiawan (Engineering, MAthematics and Computer Science Journal (EMACS), 2025)
Predicting the stock market remains a challenging task brought by the nonlinear, volatile, and dynamic nature of financial time series. While deep learning techniques have been widely applied in developed markets, studies in emerging markets such as Indonesia remain scarce. This study conducts a comparative analysis of three deep learning models—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN–LSTM—on five randomly selected constituents of the IDX30 index. The data range from January 2020 to December 2024, providing a general view of stock movement in recent years. The models were trained on daily OHLCV (Open, High, Low, Close, Volume) data, which was formatted using a sliding-window approach. Results show that LSTM achieved the lowest RMSE of 0.0222 ± 0.0030, MAE of 0.0172 ± 0.0015, and the highest R² of 0.889 ± 0.068. The Hybrid model delivered intermediate performance, improving upon CNN but not surpassing LSTM. These findings confirm that LSTM networks are particularly effective for stock price forecasting in the Indonesian market, while hybrid CNN–LSTM architectures can provide complementary strengths by balancing short-term feature learning with long-term temporal dependencies.