PERBANDINGAN ALGORITMA RANDOM FOREST DAN XGBOOST UNTUK IDENTIFIKASI PERGERAKAN HARGA EMAS

TRIADI, ALFARIS (2024) PERBANDINGAN ALGORITMA RANDOM FOREST DAN XGBOOST UNTUK IDENTIFIKASI PERGERAKAN HARGA EMAS. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This research explores the performance comparison of Random Forest (RF) and XGBoost algorithms in predicting gold price movements, which has significant implications for investment decision-making. With the urgency to improve prediction accuracy in the commodity market, this study compares the two algorithms through various evaluation metrics. The results show that XGBoost excels in the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) metrics, although both algorithms have the same coefficient of determination (R²). Visualization of the line chart and residual plot confirmed that both algorithms were able to follow the gold price movement trend well, with near-normal error distribution. Feature importance analysis shows that the "Open" feature is highly dominant in both models. In conclusion, although both algorithms performed well, XGBoost was superior in reducing prediction error, making it a more effective choice for gold price analysis. Keywords: Gold, Comparison, Random Forest, XGBoost, Penelitian ini mengeksplorasi perbandingan kinerja algoritma Random Forest (RF) dan XGBoost dalam memprediksi pergerakan harga emas, yang memiliki implikasi signifikan bagi pengambilan keputusan investasi. Dengan urgensi untuk meningkatkan akurasi prediksi di pasar komoditas, penelitian ini membandingkan kedua algoritma melalui berbagai metrik evaluasi. Hasil penelitian menunjukkan bahwa XGBoost unggul dalam metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan Mean Absolute Error (MAE), meskipun kedua algoritma memiliki koefisien determinasi (R²) yang sama. Visualisasi line chart dan plot residual mengkonfirmasi bahwa kedua algoritma mampu mengikuti tren pergerakan harga emas dengan baik, dengan distribusi kesalahan yang mendekati normal. Analisis pentingnya fitur menunjukkan bahwa fitur "Open" sangat dominan dalam kedua model. Kesimpulannya, meskipun kedua algoritma menunjukkan kinerja yang baik, XGBoost lebih unggul dalam mengurangi kesalahan prediksi, menjadikannya pilihan yang lebih efektif untuk analisis harga emas. Kata kunci: Emas, Perbandingan, Random Forest, XGBoost,

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