APPLICATION OF THE EXTREME LEARNING MACHINE (ELM) METHOD IN PREDICTING THE COMBINED STOCK PRICE INDEX (IHSG) IN INDONESIA

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.