Pengaplikasian Algoritma Simple Linear Regression untuk Prediksi Harga Rumah di Jabodetabek Berdasarkan Fitur Lokasi dan Luas Bangunan

Abstract

Penelitian ini menggunakan data sekunder yang telah melalui beberapa proses pra-pengolahan, mencakup penanganan data yang hilang, standarisasi data numerik, serta konversi data kategorikal menggunakan teknik One-Hot Encoding. Sebagian besar data (80%) digunakan dalam tahap pelatihan, sedangkan 20% sisanya digunakan untuk tahap pengujian, sedangkan model diimplementasikan dengan metode LinearRegression() pada library scikit-learn. Hasil evaluasi menunjukkan bahwa model berhasil menangkap hubungan linier di antara variabel independen dan dependen, memperoleh nilai MAE = 0,509; MSE = 0,464; RMSE = 0,681; dan R² = 0,627. Hal ini menandakan bahwa sekitar 62,7 persen variasi harga rumah di wilayah Jabodetabek dapat dijelaskan oleh model tersebut.The research utilizes secondary data that has been preprocessed through several stages, including handling missing values, standardizing numerical attributes, and converting categorical data using One-Hot Encoding. The dataset were partitioned into subsets consisting of 80% for training purposes and 20% for model validation, and the model was then built using the LinearRegression() method available in scikit-learn. Evaluation outcomes reveal that the model effectively recognizes the linear correlation between the independent and dependent variables, obtaining MAE = 0.509, MSE = 0.464, RMSE = 0.681, and R² = 0.627. This shows that approximately 62.7 percent of the variation in house prices within the Jabodetabek region can be accounted for by the model.