Open AccessDeep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG)
Author(s) -
Arief Fadhlurrahman Rasyid,
R Dewi Agushinta,
Dharma Tintri Ediraras
Publication year - 2021
Publication title -
international journal of computer and information technology
Language(s) - English
Resource type - Journals
ISSN - 2279-0764
DOI - 10.24203/ijcit.v10i5.153
Subject(s) - composite index , econometrics , stock (firearms) , stock price , composite number , index (typography) , stock market index , stock exchange , capitalization weighted index , computer science , cost price , economics , series (stratigraphy) , composite indicator , stock market , algorithm , finance , geography , biology , paleontology , context (archaeology) , archaeology , world wide web
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.