Literature Review: Metode Machine Learning dan Deep Learning dalam Prediksi Indeks Harga Saham Gabungan (IHSG)
DOI:
https://doi.org/10.55606/jutiti.v6i2.7331Keywords:
Deep Learning, Gated Recurrent Unit (GRU), Jakarta Composite Index (JCI), Long Short-Term Memory (LSTM), Systematic Literature ReviewAbstract
The Jakarta Composite Index (JCI) is a key indicator reflecting the condition of the Indonesian capital market and plays a crucial role in investment decision-making. The JCI's fluctuating, non-linear movement, influenced by various economic factors, makes prediction a challenging process. This study aims to analyze and predict JCI movements using machine learning and deep learning approaches. The methods studied include conventional time-series models and neural network-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), as well as hybrid approaches that have been widely used in previous research. This study was conducted using a systematic literature review approach in accredited national journals and reputable international journals that discuss JCI prediction. The results indicate that deep learning methods generally provide better prediction performance than traditional statistical methods, particularly in capturing long-term patterns and market volatility. This research is expected to serve as a reference basis in developing more accurate and reliable JCI prediction models.
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Aisyah, S., Angraini, Y., Sadik, K., Sartono, B., & Dito, G. A. (2024). Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network. 12(2), 187–196.
Diqi, M., Hiswati, M. E., & Wijaya, N. (2024). Stacked Gated Recurrent Units and Indonesian Stock Predictions : A New Approach to Financial Forecasting. 5(1), 11–17.
Fadhila, M. F., Anwar, R. S., & Riyandi, A. (2025). Prediksi Harga Penutupan Indeks Harga Gabungan ( IHSG ) Menggunakan Algoritma Long Short-Term Memory Berbasis Data Historis. 5(3), 28925–28934.
Ghudafa, M., Akbar, T., Panggabean, S., & Noor, M. (2022). Perbandingan Prediksi Harga Saham Dengan Menggunakan LSTM GRU Dengan Transformer. 11(1), 2020–2023.
Hadi, M. F., & Priyanto, D. (2024). Perbandingan Kinerja RNN , LSTM , dan GRU dalam. September 2025, 265–269.
Hernita, A., Sudarmojo, O. D., & Saputra, S. (2025). Information Technology Short-Term IHSG Closing Price Prediction Using Random Forest. 4(3), 402–414.
Illa, A., Nensi, E., Maida, M. Al, Notodiputro, K. A., Angraini, Y., & Nissa, L. (2025). Performance Analysis of ARIMA , LSTM , and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index. 10(2), 588–604.
Mei, N., Gempati, A., Kewilayahan, P. E., Vokasi, S., Mada, U. G., Kewilayahan, P. E., Vokasi, S., Mada, U. G., Kewilayahan, P. E., Vokasi, S., Mada, U. G., Kewilayahan, P. E., Vokasi, S., Mada, U. G., Kewilayahan, P. E., Vokasi, S., Mada, U. G., Bulaksumur, A., Depok, K., … Yogyakarta, D. I. (2025). PERAMALAN DATA IHSG 2021-2025 DI INDONESIA DENGAN TIME SERIES MODELING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE ( ARIMA ). 3(5), 225–234.
Mulyani, A. N. (2025). IMPLEMENTASI PREDIKSI HARGA SAHAM MENGGUNAKAN MODEL GATED RECURRENT UNIT (GRU) PADA PERUSAHAAN SEKTOR PROPERTI. 8(2), 457–464. https://doi.org/10.29407/noe.v8i02.24732
Prasetyo, E. J., & Hartomo, K. D. (2023). Multi-industry stock forecasting using GRU-LSTM deep transfer learning method. 15(2), 150–163.
Prices, C. (2025). Komparasi Kinerja Model LSTM dan Transformer pada Prediksi Harga Penutupan Saham NVIDIA dan Penerapan Strategi Rule - based Trading Comparison of LSTM and Transformer Models in Predicting NVIDIA Stock Closing Prices and the Application of Rule - based Trading Strategies. 14, 2424–2437.
Rasyid, A. F., R, D. A., & Ediraras, D. T. (2021). Deep Learning Methods In Predicting Indonesia Composite Stock Price Index ( IHSG ). 10(5), 209–217. https://doi.org/10.24203/ijcit.v10i5.153
Simanjuntak, G. F. (2025). Forecasting Weekly Stock Price of Jakarta Stock Exchange Composite Index ( IHSG ) Using ARIMA Box-Jenkins Method.
Siregar, B., Pangruruk, F. A., & Widjaja, P. A. (2022). Comparison of Various Forecasting Models for the Jakarta Stock Exchange Composite ( JKSE ) During the Covid-19 Pandemic Perbandingan Berbagai Model Peramalan Indeks Harga Saham Gabungan ( IHSG ) di Masa Pandemi Covid-19. 2(2), 1035–1046.
Xiao, Deng, & Bi, 2024. (n.d.). Comparative Analysis of LSTM , GRU , and Transformer Models for Stock Price Prediction. https://arxiv.org/abs/2411.05790
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