Prediction Analysis of Jakarta Composite Index Movement Using Support Vector Regression Method
DOI:
https://doi.org/10.70609/gtech.v9i1.5879Keywords:
Jakarta Composite Index, Support Vector Regression, Radial Basis FunctionAbstract
The JCI is an important indicator that reflects the performance of the Indonesian stock market. In recent times, the JCI has faced significant fluctuations due to complex factors, including global economic conditions and market sentiment, which make predicting its movements challenging. Good prediction is needed to support market stability and sustainable economic development as per SDGs point 8. This study applies a modern nonparametric regression method, namely Support Vector Regression (SVR), to predict a dataset in the form of weekly JCI data from the period April 2022 to October 2024 obtained from the investing.com website. The analysis shows that the SVR model with RBF kernel function provides the best performance, with MAPE of 1.43%, RMSE of 121.6196, and MAE of 104.65. The findings also reveal that the fluctuation pattern of the JCI cannot be fully explained based solely on historical data. External variables, such as global economic conditions and market sentiment, have a significant influence on the prediction results. Therefore, the SVR method can be utilized to optimize portfolio allocation based on weekly JCI predictions. In addition, the results of this study provide guidance for policymakers in designing proactive economic policies to mitigate market volatility and increase investor confidence.
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Copyright (c) 2025 Marcelena Vicky Galena, Sediono Sediono, M. Fariz Fadillah Mardianto, Elly Pusporani
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