PERBANDINGAN HASIL PREDIKSI HARGA PROPERTI DI DAERAH BROOKLYN MENGGUNAKAN METODE XGBOOST, RANDOM FOREST, DAN LINEAR REGRESSION
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Hukum supply and demand menyebabkan kenaikan harga properti karena permintaan yang terus meningkat untuk tempat tinggal di daerah padat penduduk seperti Brooklyn, New York. Tujuan dari penelitian ini adalah untuk menemukan model terbaik berdasarkan efisiensi dan akurasi dengan menggunakan algoritma pengajaran mesin seperti Linear Regression, Random Forest, dan XGBoost untuk memprediksi harga properti. Dataset yang digunakan terdiri dari 20.894 data penjualan properti yang memiliki atribut seperti harga penjualan, luas bangunan, dan jumlah unit. Dengan menggunakan random_state acak, eksperimen dilakukan dengan membagi data latihan dan uji menjadi 70:30. Memanfaatkan metrik R2, MAE, RMSE, dan waktu komputasi, kinerja algoritma dinilai. Hasil menunjukkan bahwa XGBoost memiliki kinerja terbaik dengan nilai R2 tertinggi (0.014) dan MAE dan RMSE terendah. Di sisi lain,Linear Regression menunjukkan bahwa XGBoost memiliki kinerja terburuk dengan nilai R2 negatif dan eror yang tinggi. Penelitian ini menemukan bahwa XGBoost memiliki kemampuan yang lebih baik untuk memodelkan data properti dan memberikan prediksi yang lebih akurat daripada metode lain. Hasil-hasil ini dapat membantu pemangku kepentingan, seperti pengembang properti dan pembeli, memahami tren harga properti dan membuat keputusan yang lebih baik berdasarkan data.
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