Metode Hybrid SVR-GWO Untuk Prediksi Harga Saham PT. Aneka Tambang Tbk - Muhammad Aditya Rahman; Taghfirul Azhima Yoga Siswa; Rofilde Hasudungan (STMIK Banjarbaru, 2026)
Fluctuating and unpredictable stock price movements pose a challenge for investors in their decision-making. This study aims to apply and analyze the performance of a hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) model in predicting the stock price of PT Aneka Tambang Tbk. The data used consists of daily stock prices from September 11, 2020, to September 11, 2025, totaling 1,202 data points, with a division of 70% training data and 30% testing data. The research stages include pre-processing, basic SVR modeling, and parameter optimization using GWO. The evaluation was carried out using RMSE, MAE, and MAPE. The results show that GWO optimization improved the model's performance from RMSE 99.78, MAE 55.70, and MAPE 2.61% to RMSE 77.27, MAE 48.97, and MAPE 2.37%. Thus, the SVR–GWO model is capable of improving the accuracy of stock price predictions and has the potential to support investment decision-making.Keyword: Grey Wolf Optimizer; Machine Learning; Prediction; Stock Price; Support Vector Re-gression AbstrakPergerakan harga saham yang fluktuatif dan sulit diprediksi menjadi tantangan bagi investor dalam pengambilan keputusan. Penelitian ini bertujuan menerapkan dan menganalisis kinerja model hybrid Support Vector Regression (SVR)–Grey Wolf Optimizer (GWO) dalam memprediksi harga saham PT Aneka Tambang Tbk. Data yang digunakan berupa harga saham harian periode 11 September 2020 hingga 11 September 2025 sebanyak 1202 data, dengan pembagian 70% data pelatihan dan 30% data pengujian. Tahapan penelitian meliputi pre-processing, pemodelan SVR dasar, serta optimasi parameter menggunakan GWO. Evaluasi dilakukan menggunakan RMSE, MAE, dan MAPE. Hasil menunjukkan bahwa optimasi GWO meningkatkan kinerja model dari RMSE 99.78, MAE 55.70, dan MAPE 2.61% menjadi RMSE 77.27, MAE 48.97, dan MAPE 2.37%. Dengan demikian, model SVR–GWO mampu meningkatkan akurasi prediksi harga saham dan berpotensi mendukung pengambilan keputusan investasi.Kata Kunci: Grey Wolf Optimizer; Harga Saham; Machine Learning; Prediksi; Support Vector Regression