MACHINE LEARNING UNTUK PREDIKSI HARGA SAHAM DENGAN VALIDASI STRATEGI TRADING MENGGUNAKAN BACKTESTING PADA SEKTOR PERBANKAN DI BURSA EFEK INDONESIA - Nurkalam, Haikal; Sulaiman, Maulana Muhamad (Pusat Pengembangan Pendidikan dan Penelitian Indonesia, 2026)
Stock investment in the Indonesian banking sector faces challenges in predicting volatile price movements, especially for novice investors with limited literacy and experience. This research aims to build a banking stock price prediction system using the Random Forest algorithm with trading strategy validation through automatic backtesting. The system was developed using 22 technical indicators (Moving Average, RSI, MACD, Bollinger Bands, and price action) to predict stock prices of Indonesia's five largest banks (BBCA, BBRI, BMRI, BBNI, BBTN) across multiple prediction horizons (1, 2, 3, 5, 10, and 30 days). Evaluation was conducted on 30 combinations (5 stocks × 6 horizons) using MAPE, MAE, RMSE, and R² score metrics. Results show the model achieves excellent accuracy with an average MAPE of 2.05% for short-term predictions (H+1) and 5.27% for medium-term predictions (H+5), categorized as