OPTIMASI XGBOOST DENGAN GRID SEARCH CROSS VALIDATION UNTUK PENINGKATAN AKURASI PREDIKSI HARGA RUMAH
DOI:
https://doi.org/10.32699/biner.v5i2.11325Keywords:
XGBoost, GridSearchCV, House Price Prediction, Machine Learning., Hyperparameter TuningAbstract
Harga rumah dipengaruhi oleh berbagai faktor seperti lokasi, luas bangunan, dan fasilitas sehingga prediksi harga secara akurat menjadi tantangan dalam sektor properti. Penelitian ini bertujuan mengoptimalkan prediksi harga rumah menggunakan algoritma XGBoost Regressor dengan Grid Search Cross Validation (GridSearchCV). Dataset yang digunakan adalah Jakarta House Price Dataset dengan tahapan preprocessing berupa penanganan missing value, transformasi logaritmik pada variabel target, dan One-Hot Encoding pada fitur kategorikal. Model dibangun dalam pipeline terintegrasi dan dioptimasi untuk memperoleh kombinasi hyperparameter terbaik. Hasil penelitian menunjukkan bahwa model yang diusulkan memperoleh nilai R² sebesar 0,8889, MAE sebesar 0,2418, MSE sebesar 0,1300, dan RMSE sebesar 0,3606, serta memiliki kinerja yang lebih baik dibandingkan Random Forest. Kontribusi penelitian ini terletak pada integrasi preprocessing dan optimasi hyperparameter XGBoost dalam satu pipeline untuk meningkatkan akurasi prediksi harga rumah pada dataset properti Indonesia. Dengan demikian, model yang dihasilkan dapat digunakan sebagai pendukung pengambilan keputusan di bidang properti.
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