Studi Penerapan Deep Learning untuk Prediksi Pergerakan Indeks Harga Saham Gabungan
Downloads
This study aims to develop and evaluate a prediction model for the movement of the Composite Stock Price Index (JCI) using the Long Short-Term Memory (LSTM) method as one of the effective deep learning approaches in capturing nonlinear time series patterns. This study tested three model approaches, namely the Univariate model which only uses historical JCI data, the Multivariate All Feature model which integrates all external variables, and the Multivariate Selected Feature model which uses selected external variables. External variables considered include world gold prices, world oil prices, rupiah exchange rates against the United States dollar, and international stock indices that affect the Indonesian capital market. The model performance evaluation was carried out using the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) indicators. The results showed that the Multivariate All Feature model provided the best performance with a MAPE value of 0.76, an RMSE of 66.72, and an MAE of 51.58, which was consistently lower than the other two models. Significance tests using ANOVA and Tukey HSD confirmed significant performance differences between models. These findings indicate that the integration of external variables is able to significantly increase the accuracy of JCI predictions. This research is expected to be a reference for investors, capital market analysts, and researchers in the development of artificial intelligence-based decision support systems in the Indonesian stock market.
Bareksa. (2024). Rebalancing MSCI dorong asing nett sell Rp1,46 T, IHSG turun 2,5%. Diakses dari https://www.bareksa.com/berita/pasar-modal/2015-11-01/rebalancing-msci-dorong-asing-nett-sell-rp146-t-ihsg-turun-25
Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451–468. https://doi.org/10.1057/jors.1969.92
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.
Chabachib, H. M., & Witjaksono, A. A. (2011). Analisis pengaruh fundamental makro dan indeks harga global terhadap IHSG. Universitas Indonesia.
Gao, T., Chai, Y., & Liu, Y. (2018). Applying long short-term memory neural networks for predicting stock closing price. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS), 575–578. https://doi.org/10.1109/ICSESS.2017.8342981
Gumilang, R. C. (2014). Pengaruh variabel makro ekonomi, harga emas dan harga minyak dunia terhadap Indeks Harga Saham Gabungan (Studi pada Bursa Efek Indonesia periode 2009–2013). Skripsi, Fakultas Ilmu Administrasi, Universitas Brawijaya.
Hassan, M. K. (2019). Predicting stock market movements: A study of the Indonesian stock market. International Journal of Economics and Financial Issues, 9(1), 85–92. https://doi.org/10.2139/ssrn.3342385
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A prediction approach for stock market volatility based on time series data. IEEE Access, 7, 17287–17298. https://doi.org/10.1109/ACCESS.2019.2895252
Invesnow. (2024). MSCI reduces Indonesia’s weight in Emerging Markets Index: Impact on IHSG. Diakses dari https://invesnow.id/artikel/msci-reduces-indonesias-weight
Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. FAU Discussion Papers in Economics, 03/2016.
Madi, I. (2018). Forecasting stock market index using ARIMA and LSTM. Journal of Financial Studies, 12(4), 55–68. https://doi.org/10.1080/21427844.2018.1456781
Makridakis, S., & Hibon, M. (1997). The accuracy of forecasting: An evaluation of 120 forecasting methods. International Journal of Forecasting, 5(4), 455–469. https://doi.org/10.1016/0169-2070(89)90022-3
Putra, D. A., & Nurmatias. (2024). Analisis Indeks Harga Saham Global dan Inflasi Terhadap Indeks Harga Saham Gabungan (IHSG). IKRAITH-EKONOMIKA, 7(1), 93–101. https://doi.org/10.37817/ikraith-ekonomika.v7i1
Sezer, M., Gudelek, M. I., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review (2005–2019). Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019). A comparison of ARIMA and LSTM in forecasting time series. Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
Theil, H. (1966). Applied economic forecasting. Rand McNally.
Copyright (c) 2025 Irene Karijadi, Yoseph Widjaja, Dian Retno Sari Dewi, Luh Juni Asrini
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-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement 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 acknowledgement 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.
