Implementasi XGBoost dan Logika Reverse Calculation pada Sistem Estimasi Harga Beli Mobil Bekas Berbasis Web - Ida Bagus Aditya Cahya Wiraguna; Putu Indah Ciptayani; I Putu Bagus Arya Pradnyana; Ni Gusti Ayu Putu Harry Saptarini (Universitas Methodist Indonesia, 2026)
The movement of used car prices is highly dynamic and subjective, making the manual estimation process prone to bias and financial risk. This study aims to develop a web-based used car purchase price estimation system using the Extreme Gradient Boosting (XGBoost) algorithm combined with a reverse calculation logic. The dataset was obtained from secondary market data and primary showroom transaction records, totaling 12,324 clean data after passing the Grouped-IQR outlier filtering process. The XGBoost model was optimized using Grid Search and validated through 10-Fold Cross-Validation. The results showed that the optimal model configuration achieved a Mean Absolute Percentage Error (MAPE) of 11.23%, a Root Mean Squared Error (RMSE) of Rp 54,779,437, and a Coefficient of Determination (R2) of 0.8386. This performance indicates a highly accurate forecasting capability. The model was successfully integrated into a Laravel-based web application via a Python REST API, allowing users to obtain fair market price predictions and maximum purchase bids to improve the efficiency and objectivity of decision-making.