A Robustness-Oriented Evaluation of LSTM, GRU, and Hybrid LSTM-GRU Models for ANTM.JK Stock Price Forecasting
DOI:
https://doi.org/10.63158/journalisi.v8i3.1660Keywords:
Stock price prediction, deep learning, LSTM, GRU, hybrid model, robustness analysis, COVID-19, ANTM.JKAbstract
Accurately forecasting stock prices remains challenging because of the nonlinear and volatile nature of financial markets, particularly during periods of heightened uncertainty, such as the COVID-19 pandemic. This study evaluates the robustness of three models, LSTM, GRU, and Hybrid LSTM-GRU, for ANTM.JK stock price forecasting using a volatility-oriented evaluation framework. Historical stock data from September 2005 to May 2022 were transformed into supervised time-series datasets using a 15-lag sliding window. The model performance was evaluated using baseline prediction accuracy, 5-fold chronological cross-validation consistency, and synthetic stress scenarios consisting of controlled price drops, price rises, and high-volatility noise. Evaluation metrics included RMSE, MSE, MAE, R, and R^2. The GRU model delivered the top baseline prediction results, achieving the smallest RMSE of 52.95 and MAE of 28.14. In cross-validation, the LSTM model recorded the lowest average RMSE of 119.41. Meanwhile, the Hybrid LSTM-GRU exhibited the highest prediction consistency and robustness across various synthetic stress scenarios. In contrast to earlier research that mainly focused on prediction precision, this study presents a comprehensive framework for evaluating robustness. This framework combines baseline accuracy, consistency through cross-validation, and an analysis of synthetic stress scenarios. The generated robustness map offers a systematic interpretation of model strengths across diverse evaluation goals, facilitating a more thorough assessment of stock-forecasting models in different market environments.
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[1] S. Kumar and D. Ningombam, “Short-Term Forecasting of Stock Prices Using Long Short Term Memory,” Proceedings - 2018 International Conference on Information Technology, ICIT 2018, pp. 182–186, Dec. 2018, doi: 10.1109/ICIT.2018.00046.
[2] N. Solanki and M. Jha, “A Decade of Stock Data: Predictive Modelling with Hybrid CNN-BiLSTM Technique,” in 2024 IEEE Silchar Subsection Conference, SILCON 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/SILCON63976.2024.10910869.
[3] T. Sanboon, K. Keatruangkamala, and S. Jaiyen, “A Deep Learning Model for Predicting Buy and Sell Recommendations in Stock Exchange of Thailand using Long Short-Term Memory,” pp. 757–760, Sep. 2019, doi: 10.1109/ccoms.2019.8821776.
[4] Y. Sharma, A. Kumar, V. Dubey, and V. Rai, “Stock Price Prediction Using LSTM,” 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, vol. 2, no. 4, pp. 1–5, Jun. 2023, doi: 10.1109/ICCCNT56998.2023.10307212.
[5] Y. Zeng, J. Chen, N. Jin, X. Jin, and Y. Du, “Air quality forecasting with hybrid LSTM and extended stationary wavelet transform,” Build. Environ., vol. 213, Apr. 2022, doi: 10.1016/j.buildenv.2022.108822.
[6] Khoirudin, P. T. Pungkasanti, and N. Wakhidah, “ANTM.JK Stock Price Prediction Using Long Short-Term Memory (LSTM) Method During COVID-19 Pandemic,” 1st International Conference on Technology, Engineering, and Computing Applications: Trends in Technology Development in the Era of Society 5.0, ICTECA 2023, pp. 1–5, 2023, doi: 10.1109/ICTECA60133.2023.10490699.
[7] Tradingview, “Aneka Tambang TBK,” tradingview. Accessed: May 12, 2026. [Online]. Available: https://id.tradingview.com/symbols/IDX-ANTM/
[8] Y. Ramdhani and A. Mubarok, “Analisis Time Series Prediksi Penutupan Harga Saham Antm.Jk Dengan Algoritma SVM Model Regresi,” Jurnal Responsif: Riset Sains dan Informatika, vol. 1, no. 1, pp. 77–82, 2019, [Online]. Available: http://ejurnal.univbsi.id/index.php/jti
[9] S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, IEEE, 2017, pp. 1643–1647. doi: 10.1109/ICACCI.2017.8126078.
[10] K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 1724–1734, Jun. 2014, doi: 10.3115/v1/d14-1179.
[11] N. K. Manaswi, “RNN and LSTM,” in Deep Learning with Applications Using Python, Apress, 2018, pp. 115–126. doi: 10.1007/978-1-4842-3516-4_9.
[12] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” International Journal of Pure and Applied Mathematics, vol. 119, no. 17, pp. 2591–2600, 2018, Accessed: Jul. 18, 2022. [Online]. Available: http://www.acadpubl.eu/hub/
[13] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208.
[14] R. Khaldi, A. El Afia, R. Chiheb, and S. Tabik, “What is the best RNN-cell structure to forecast each time series behavior?,” Expert Syst. Appl., vol. 215, Apr. 2023, doi: 10.1016/j.eswa.2022.119140.
[15] X. Zhang, C. Zhong, J. Zhang, T. Wang, and W. W. Y. Ng, “Robust recurrent neural networks for time series forecasting,” Neurocomputing, vol. 526, pp. 143–157, Mar. 2023, doi: 10.1016/j.neucom.2023.01.037.
[16] Q. Ge, “Enhancing stock market Forecasting: A hybrid model for accurate prediction of S&P 500 and CSI 300 future prices,” Expert Syst. Appl., vol. 260, p. 125380, 2025, doi: 10.1016/j.eswa.2024.125380.
[17] F. K. Mirza, Ö. Pekcan, M. Hekimoğlu, and T. Baykaş, “Stock price forecasting through symbolic dynamics and state transition graphs with a convolutional recurrent neural network architecture,” Neural Comput. Appl., 2025, doi: 10.1007/s00521-025-11325-z.
[18] N. K. Manaswi, “Deep Learning with Applications Using Python,” Deep Learning with Applications Using Python, 2018, doi: 10.1007/978-1-4842-3516-4.
[19] D. R. Danistya, F. Qaulifa, Y. A. Ramadani, I. Nurma Yulita, M. N. Ardisasmita, and D. Agustian, “Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods,” 2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, pp. 127–130, 2021, doi: 10.1109/ICAIBDA53487.2021.9689721.
[20] A. El Filali, E. H. Ben Lahmer, S. El Filali, M. Kasbouya, M. A. Ajouary, and S. Akantous, “Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 2, pp. 464–478, Apr. 2022, doi: 10.22266/ijies2022.0430.42.
[21] M. Li, Y. Zhu, Y. Shen, and M. Angelova, “Clustering-enhanced stock price prediction using deep learning,” World Wide Web, vol. 26, no. 1, pp. 207–232, Jan. 2023, doi: 10.1007/s11280-021-01003-0.
[22] A. K. Badri, J. Heikal, Y. A. Terah, and D. R. Nurjaman, “Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia,” APTISI Transactions on Management (ATM), vol. 6, no. 2, pp. 167–180, 2021, doi: 10.33050/atm.v6i2.1776.
[23] N. G. Ramadhan, N. A. F. Tanjung, and F. D. Adhinata, “Implementation of LSTM-RNN for Bitcoin Prediction,” Ind. Journal on Computing, vol. 6, no. 3, pp. 17–24, 2021, doi: 10.34818/indojc.2021.6.3.592.
[24] “PT Aneka Tambang Tbk (ANTM.JK) Stock Historical Prices Data,” Yahoo Finance. Accessed: Oct. 03, 2023. [Online]. Available: https://finance.yahoo.com/quote/ANTM.JK
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