Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG)
Keywords:Composite Stock Price Index (IHSG), Deep learning, GRU, Jakarta Composite Index (^JKSE), LSTM, predicting, stock price
The stock price changes at any time within seconds. The stock price is a time series data. Thus, it is necessary to have the best analysis model in predicting the stock price to make decisions to avoid losses in investing. In this research, the method used two models Deep Learning namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting Indonesia Composite Stock Price Index (IHSG). The dataset used is historical data from the Jakarta Composite Index (^JKSE) stock price in 2013-2020 obtained through Yahoo Finance. The results suggest that Deep learning methods with LSTM and GRU models can predict Indonesia Composite Stock Price Index (IHSG). Based on the test results obtained RMSE value of 71.28959454502723 with an accuracy rate of 92.39% for LSTM models and obtained RMSE value of 70.61870739073838 with an accuracy rate of 96.77% on GRU models.
L. P. Putri, “Pengaruh Profitabilitas Terhadap Harga Saham Pada Perusahaan Pertambangan Batubara di Indonesia,” Jurnal Ilmiah Manajemen dan Bisnis, 16(2), 2015, pp. 49-59.
V. Harfikawati, “Pengaruh Tingkat Inflasi Nilai Tukar Rupiah Terhadap USD, dan Indeks Dow Jones Terhadap IHSG Di Bursa Efek Indonesia Tahun 2011-2015,” Journal Eksekutif, 13(2), 2016.
L. E. Siahaan, Prediksi Indeks Harga Saham dengan Metode Gabungan Support Vector Regression dan Jaringan Syaraf Tiruan. Indonesia Journal on Computing (Indo-JC), 2(1), 2017, pp. 21-30.
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. “Deep learning for stock market prediction,” Entropy, 22(8), 2020, p. 840.
J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism.” PLoS ONE 15(1): e0227222. 2020, https://doi.org/10.1371/journal.pone.0227222.
L. Deng, and D. Yu, “Deep Learning: Methods and Applications,” Foundations and Trends in Signal Processing, 7(3–4), 2014, pp. 197–387.
Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, 9(8), 1997, pp. 1735–1780.
A. S. B. Karno, and W. Hastomo, “Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long Short Term Memory),” Journal of Informatic and Information Security, 1(1), 2020, pp. 1-8.
R. Yotenka, and F. F. El Huda, “Implementasi Long Short-Term Memory Pada Harga Saham Perusahaan Perkebunan Di Indonesia,” Unisda Journal of Mathematics and Computer Science (UJMC), 6(01), 2020, pp. 9-18.
S. Zahara, and M. B. Ilmiddafiq, “Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing,” Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 2019, pp. 357-363.
E. Mahpudin, and N. A. Mahmud, “Analysis of The Contribution of Tax Increases In Changes of Stock Prices In Indonesia and The United States,” Journal of Critical Reviews, 7(11), 2020, pp. 2194-2197.
M. R. Hutauruk, and I. Ghozali, “Overview of return on investment on cigarette companies registered in Indonesia stock exchange,” International Journal of Scientific and Technology Research, 9(03), 2020, pp. 4633-4637.
J. Brownlee, “Develop Your First Neural Network in Python With Keras Step-By-Step,” Machine Learning Mastery, 2016.
L. Zaman, S. Sumpeno, and M. Hariadi, “Analisis Kinerja LSTM dan GRU sebagai Model Generatif untuk Tari Remo,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 8(2), 2019, pp. 142-150.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” in NIPS 2014 Workshop on Deep Learning, December 2014.
N. Donges, “Towards Data Science,” Gradient Descent in a Nutshell. https://towardsdatascience.com/gradient-descent-in-a-nutshell eaf8c18212f0, 2018.
F. Chollet, and J. J. Allaire, Deep Learning with R, Vol. 1, Manning Publications Co. NY, 2018.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean,... and X. Zheng, “Tensorflow: A system for large-scale machine learning,” in Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (OSDI 16), 2016, pp. 265-283.
M. Mada, A. Farmadi, I. Budiman, M. R. Faisal, and M. I. Mazdadi, “GRU, AdaGrad, RMSprop, Adam Implementation of GRU and Adam Optimization Method for Stock Price Prediction,” Journal of Data Science and Software Engineering, 2(01), 2021, pp. 36-45.
L. J. Tashman, Out-of-sample tests of forecasting accuracy: an analysis and review. International journal of forecasting, 16(4), 2000, pp. 437-450.
A. De Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean absolute percentage error for regression models,” Neurocomputing, 192, 2016, pp. 38-48.
How to Cite
Copyright (c) 2021 Arief Fadhlurrahman Rasyid, Dewi Agushinta R., Dharma Tintri Ediraras
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.