DOI: https://doi.org/10.14710/j.gauss.15.2.356-367

PERBANDINGAN PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE REGRESI RANDOM FOREST DAN EXTREME GRADIENT BOOSTING

Agnes Monika Simalango  -  Program Studi Sains Aktuaria, Fakultas Sains, Institut Teknologi Sumatera, Indonesia
*Muklas Rivai scopus  -  Institut Teknologi Sumatera, Indonesia
Fuji Lestari  -  Program Studi Sains Aktuaria, Fakultas Sains, Institut Teknologi Sumatera, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Indeks Harga Saham Gabungan (IHSG) serves as the benchmark of the Indonesian stock market and is characterized by nonlinear and volatile movements driven by macroeconomic conditions, trading activities, and market sentiment. This study aimed to compare the forecasting performance of Random Forest and Extreme Gradient Boosting (XGBoost) for the IHSG using historical data from January 2019 to July 2025 with an 80:20 chronological train-test split. Model performance was evaluated using RMSE and MAPE, followed by variable importance analysis and Value at Risk (VaR) estimation to asses potential investment losses. The results showed that Random Forest outperformed XGBoost, achieving an RMSE of 117.54 and a MAPE of 0.90%, whereas XGBoost produced an RMSE of 136.45 and a MAPE of 1.03%. Variable importance analysis identified the highest index value as the most influential predictor, followed by the lowest index value, the opening index value, and trading volume. Furthermore, the estimated expected return was 0.00014, with VaR values of IDR 470,000.00, IDR 610,000.00, and IDR 960,000.00 at the 90%, 95%, and 99% confidence levels, respectively. Based on the evaluation results, Random Forest achieved better forecasting accuracy than XGBoost for the IHSG.
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Keywords: IHSG; Random Forest; Extreme Gradient Boosting; Prediction; Variable Importance

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