Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG)


  • Arief Fadhlurrahman Rasyid Gunadarma University, West Java, Indonesia
  • Dewi Agushinta R. Gunadarma University, West Java, Indonesia
  • Dharma Tintri Ediraras Gunadarma University, West Java, Indonesia



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.

Author Biography

Dewi Agushinta R., Gunadarma University, West Java, Indonesia

Information Technology Departement

Gunadarma University


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How to Cite

Arief Fadhlurrahman Rasyid, Dewi Agushinta R., & Dharma Tintri Ediraras. (2021). Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG). International Journal of Computer and Information Technology(2279-0764), 10(5).