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All Journal Tadulako Social Science and Humaniora Journal
Nur Azizah Janad
Statistics Study Program, Faculty of Mathematics and Natural Sciences, Tadulako University
Religion Arts Humanities Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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APPLICATION OF THE EXTREME LEARNING MACHINE (ELM) METHOD IN PREDICTING THE COMBINED STOCK PRICE INDEX (IHSG) IN INDONESIA Nur Azizah Janad; Junaidi; Iman Setiawan
Tadulako Social Science and Humaniora Journal Vol. 2 No. 2 (2022): Tadulako Social Science and Humaniora Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sochum.v2i2.17382

Abstract

The stock market is one area that continues to attract the attention of investors and financial researchers. This research explores the application of the Extreme Learning Machine (ELM) method to predict the Composite Stock Price Index (IHSG) in Indonesia. ELM is known for its fast learning capabilities with minimal prerequisite network architecture. In this research, three types of activation functions, namely Sigmoid, ReLU, and Tanh, are applied to ELM to compare their performance in predicting IHSG. Monthly IHSG data is used for model training and testing. Data preprocessing steps, such as dividing the data into Training and Test sets, are applied before feeding it into the model. Model performance was evaluated using Root Mean Square Error (RMSE) and compared for each activation function. The research results show that each activation function has a different impact on the IHSG prediction performance. In this research, the ReLU activation function showed the best performance in predicting IHSG compared to other activation functions, with a Root Mean Square Error (RMSE) of 1 x 10-16. These results show that the model's predictive performance in estimating actual values is very good.
Co-Authors Iman Setiawan Junaidi