Hybrid Support Vector Regression-Genetic Algorithm Model for Forecasting Stock Prices - Muhammad Ulil Albab; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2026)
The stock market exhibits a high level of volatility, which often leads to significant price fluctuations and increases the risk of financial losses for investors. Therefore, stock price prediction is an important tool to support investment decision-making, particularly for PT Aneka Tambang Tbk (ANTM.JK). This study aims to predict ANTM stock prices by applying the Support Vector Regression (SVR) method optimized using a Genetic Algorithm (GA). The data used in this study consist of 1202 historical stock price data of ANTM from September 11, 2020 to September 11, 2025, obtained from Investing.com, and the data are normalized using the Min-Max normalization method. The dataset is divided into training data and testing data using an 80:20 ratio, where 80% of the data are used for training and 20% for testing. The SVR model is constructed using the Radial Basis Function (RBF) kernel, while the GA is employed to optimize the SVR parameters in order to obtain the optimal parameter combination, with main GA parameters including population size of 50, 30 generations, crossover rate of 0.8, and mutation rate of 0.1. Model performance is evaluated by comparing the prediction results of SVR without optimization and GA-optimized SVR using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results indicate that the application of the GA improves the predictive performance of the model. The SVR model without optimization produces RMSE, MAE, and MAPE values of 85.48, 59.02, and 2.62%, respectively. After parameter optimization using GA, the model performance improves as indicated by reduced error values, with RMSE of 75.97, MAE of 52.42, and MAPE of 2.42%