Analisis Faktor Penentu Harga Mobil Bekas Menggunakan Model Random Forest Regressor serta Perbandingan Linear


Sabrina Khoirunnisa(1*); Nining Rahaninsih(2); Irfan Ali(3); Willy Prihartono(4);

(1) STMIK IKMI
(2) STMIK IKMI
(3) STMIK IKMI
(4) STMIK IKMI
(*) Corresponding Author

  

Abstract


Penentuan harga mobil bekas merupakan proses yang kompleks karena dipengaruhi oleh berbagai faktor seperti jarak tempuh, usia kendaraan, merek, jenis bahan bakar, dan riwayat kerusakan. Metode konvensional sering kali menghasilkan penilaian yang subjektif dan kurang akurat, sehingga diperlukan pendekatan berbasis data untuk meningkatkan objektivitas dan konsistensi estimasi harga. Penelitian ini bertujuan untuk membangun model prediksi harga mobil bekas menggunakan algoritma Random Forest Regressor serta membandingkan performanya dengan Multiple Linear Regression sebagai baseline. Dataset yang digunakan berasal dari platform Kaggle dan mencakup 4.009 data kendaraan yang telah melalui proses data cleaning, rekayasa fitur, dan penghapusan outlier. Metode penelitian meliputi preprocessing data, pelatihan model, hyperparameter tuning, serta evaluasi menggunakan metrik R², MAE, MSE, dan RMSE. Hipotesis penelitian menyatakan bahwa Random Forest memiliki performa prediktif yang lebih baik dibandingkan model linier serta mampu mengidentifikasi fitur yang paling berpengaruh terhadap harga kendaraan. Hasil eksperimen menunjukkan bahwa Random Forest R² = 0.6827, lebih tinggi dibandingkan Multiple Linear Regression dengan R² = 0.5673. Analisis feature importance mengungkapkan bahwa mileage dan usia kendaraan merupakan faktor dominan dalam pembentukan harga. Dengan demikian, penelitian ini menyimpulkan bahwa Random Forest merupakan pendekatan yang lebih akurat dan stabil untuk prediksi harga mobil bekas serta berpotensi diimplementasikan dalam sistem valuasi otomotif berbasis data.


Keywords


prediksi harga mobil; machine learning; random forest; regresi linier; feature importance

  
  

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