Perbandingan Kinerja Algoritma Random Forest dan Xgboost Dalam Memprediksi Harga Bitcoin Menggunakan Indikator Teknikal

The extreme volatility of crypto assets makes Bitcoin price movements notoriously difficult to predict using conventional linear forecasting approaches. This study aims to compare the performance of two machine learning algorithms Random Forest Regressor and XGBoost Regressor in projecting Bitcoin's closing price. A quantitative approach was employed using two years of historical time-series data, with the Simple Moving Average (SMA) and Relative Strength Index (RSI) serving as primary predictor features; both models were then comprehensively evaluated using absolute and percentage error metrics. Experimental results demonstrate that the XGBoost Regressor significantly outperformed the other model, achieving superior accuracy by minimizing error margins (MAE: 2327.81; RMSE: 2978.93; MAPE: 1.99%; R2: 0.4571), whereas the Random Forest model struggled to accurately capture volatile fluctuations (MAE: 2891.19; RMSE: 3619.53; MAPE: 2.53%; R2: 0.1984). Furthermore, the findings reveal that both algorithms largely disregarded momentum indicators, basing their predictions almost entirely on short-term average trend movements. In conclusion, XGBoost is empirically proven to be a far more robust and precise algorithm than Random Forest for modeling the anomaly-prone price dynamics of Bitcoin. The primary contribution of this research lies in providing empirical evidence regarding the effectiveness of the boosting paradigm over bagging for highly volatile crypto assets, as well as the direct implementation of the winning prediction architecture into an interactive Streamlit based dashboard to facilitate practical, real world investment decision making.