Comparative Analysis of CNN, LSTM, and CNN–LSTM for Indonesian Stock Prediction
DOI:
https://doi.org/10.21512/emacsjournal.v7i3.14326Keywords:
CNN, LSTM, Deep Learning, Indonesian Stock Exchange (IDX30), Stock Market PredictionAbstract
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.
Plum Analytics
References
Aswini, J., S, D., Lakshmipriya, C., M, L. K., & R, S. S. (2024). Stock Market Forecasting Using LSTM. 2024 2nd World Conference on Communication & Computing (WCONF), 1–4. https://doi.org/10.1109/WCONF61366.2024.10692057
Bhardwaj, A., & Singh, U. P. (2023). An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models. Proceedings of the 5th International Conference on Information Management & Machine Intelligence, 1–10. https://doi.org/10.1145/3647444.3652440
Choudhury, S., Basak, S., Roy, S., & Das, A. K. (2023). Long-Short Term Memory Based Stock Market Analysis. 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), 1–5. https://doi.org/10.1109/IEMENTech60402.2023.10423465
Dadhich, M., Pahwa, M. S., Jain, V., & Doshi, R. (2021). Predictive Models for Stock Market Index Using Stochastic Time Series ARIMA Modeling in Emerging Economy. In G. Manik, S. Kalia, S. K. Sahoo, T. K. Sharma, & O. P. Verma (Eds.), Advances in Mechanical Engineering (pp. 281–290). Springer Singapore. https://doi.org/10.1007/978-981-16-0942-8_26
Fadziso, T. (2020). Overcoming the Vanishing Gradient Problem during Learning Recurrent Neural Nets (RNN). Asian Journal of Applied Science and Engineering, 9(1), 197–208. https://doi.org/10.18034/ajase.v9i1.41
Gupta, A., Akansha, Joshi, K., Patel, M., & Pratap, Ms. V. (2023). Stock Prices Prediction Using Machine Learning. 2023 2nd International Conference for Innovation in Technology (INOCON), 1–7. https://doi.org/10.1109/INOCON57975.2023.10101226
Hong, H., Bian, Z., & Lee, C.-C. (2021). COVID-19 and instability of stock market performance: Evidence from the U.S. Financial Innovation, 7(1), 12. https://doi.org/10.1186/s40854-021-00229-1
Jiang, M., Wang, K., Sun, Y., Chen, W., Xia, B., & Li, R. (2023). MLGN: Multi-scale local-global feature learning network for long-term series forecasting. Machine Learning: Science and Technology, 4(4), 045059. https://doi.org/10.1088/2632-2153/ad1436
Joshi, S., Mahanthi, B. L., G, P., Pokkuluri, K. S., Ninawe, S. S., & Sahu, R. (2025). Integrating LSTM and CNN for Stock Market Prediction: A Dynamic Machine Learning Approach. Journal of Artificial Intelligence and Technology. https://doi.org/10.37965/jait.2025.0652
Laxmi Narayan, M. D., & Balaji, N. (2025). Stock Opening Price Prediction Using RNN and LSTM. In H. Sharma, A. Chakravorty, S. Hussain, & R. Kumari (Eds.), Artificial Intelligence: Theory and Applications (Vol. 5589, pp. 363–376). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-1687-9_25
Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-Based Model to Forecast Stock Prices. Complexity, 2020, 1–10. https://doi.org/10.1155/2020/6622927
Mane, V., Patil, R., Pawar, S., Pujari, N., Jaggi, A., & Kakkar, P. (2025). Time Series Forecasting of Stock Prices Using Neural Networks LSTM and GAN. 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET), 1–5. https://doi.org/10.1109/ICAET63349.2025.10932260
Marisetty, N. (2024). Prediction of Popular Global Stock Indexes Volatility by Using ARCH/GARCH Models. SSRN. https://doi.org/10.2139/ssrn.4904475
Mezghani, T., & Abbes, M. B. (2023). Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks. Asia-Pacific Financial Markets, 30(3), 505–530. https://doi.org/10.1007/s10690-022-09387-3
Noh, S.-H. (2021). Analysis of Gradient Vanishing of RNNs and Performance Comparison. Information, 12(11), 442. https://doi.org/10.3390/info12110442
Shah, J., Vaidya, D., & Shah, M. (2022). A comprehensive review on multiple hybrid deep learning approaches for stock prediction. Intelligent Systems with Applications, 16, 200111. https://doi.org/10.1016/j.iswa.2022.200111
Srivastava, A., Srivastava, A., Singh, Y. B., & Misra, M. K. (2024). Deep Learning Models for Stock Market Forecasting: GARCH, ARIMA, CNN, LSTM, RNN. In A. Chaturvedi, S. U. Hasan, B. K. Roy, & B. Tsaban (Eds.), Cryptology and Network Security with Machine Learning (Vol. 918, pp. 827–839). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0641-9_56
Xiao, C., & Sun, J. (2021). Recurrent Neural Networks (RNN). In C. Xiao & J. Sun, Introduction to Deep Learning for Healthcare (pp. 111–135). Springer International Publishing. https://doi.org/10.1007/978-3-030-82184-5_7
Zhao, Q., Hao, Y., & Li, X. (2024). Stock price prediction based on hybrid CNN-LSTM model. Applied and Computational Engineering, 104(1), 110–115. https://doi.org/10.54254/2755-2721/104/20241065
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Setiawan Joddy

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)