Implementasi XGBoost dan Logika Reverse Calculation pada Sistem Estimasi Harga Beli Mobil Bekas Berbasis Web
DOI:
https://doi.org/10.46880/jmika.Vol10No1.pp357-365Keywords:
XGBoost, Price Estimation, Used Cars, Machine Learning, Web-Based SystemAbstract
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.
References
Ariyono, L. D., & Manafe, L. A. (2022). Penetapan Harga Jual Produk Terhadap Volume Penjualan Pada CV Vulkanindo Mitra Abadi. Business and Enterpreneurship Journal (BEJ), 3(1), 40–47.
Arradian, D. (2025, January 22). Gaikindo: Penjualan Mobil Baru 865.723 Unit, Mobil Bekas 1,8 Juta di 2024. SindoNews. https://otomotif.sindonews.com/
Bolikulov, F., Nasimov, R., Rashidov, A., Akhmedov, F., & Cho, Y. I. (2024). Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms. Mathematics, 12(16). https://doi.org/10.3390/math12162553
Eka Adinagoro, R., Dwi Rangga Premana, I., Bintang Pamungkas, R., Aryasetya, P., & Joedhistiro, D. (2025). Pengembangan Sistem Prediksi Harga Mobil Bekas Di Pasar India Menggunakan Algoritma XGBoost dan NLP. Prosiding Seminar Nasional Teknologi Informasi Dan Bisnis (SENATIB) 2025, 207–215. https://doi.org/10.47701/w6511j19
Fayyaz, I., Ali, G. G. M. N., & Khairunnesa, S. S. (2025). Advanced Feature Engineering and Machine Learning Techniques for High Accurate Price Prediction of Heterogeneous Pre-Own Cars. Vehicles, 7(3), 1–30. https://doi.org/10.3390/vehicles7030094
Ghaisani, M. S., & Baita, A. (2025). Implementation of the K-Nearest Neighbors (KNN) Regressor Method to Predict Toyota Used Car Prices. Journal of Applied Informatics and Computing (JAIC), 9(1), 194–201.
Haya, F., & Nudin, S. R. (2024). Sistem Informasi Prediksi Harga Mobil Bekas Menggunakan Metode KNN. Jurnal Manajemen Informatika (JMI), 13(02), 8–12.
Idris, N. O., & Pontoiyo, F. (2025). Evaluasi Model Machine Learning untuk Prediksi Harga Mobil dengan Perbandingan Ensemble dan Regresi Linear. Jurnal Ilmu Komputer Dan Sistem Informasi (JIRSI), 4(1), 129–143. https://doi.org/10.70340/jirsi.v4i3
Pargent, F., Pfisterer, F., Thomas, J., & Bischl, B. (2022). Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, 37(5), 2671–2692. https://doi.org/10.1007/s00180-022-01207-6
Safitri, O., Mikhratunnisa, & Rizqi, R. M. (2024). Model Gompertz pada Depresiasi Aset Kendaraan Mobil Honda Brio. Jurnal Ilmiah Raflesia Akuntansi, 10(2), 988–994. https://doi.org/10.53494/jira.v10i2.680
Syafarina, G. A., Amin, M., & Zaenuddin. (2026). Perbandingan Tree-Based Ensemble (Random Forest, XGBoost, LightGBM) dan Deep Learning untuk Prediksi Klik Iklan Online. Jurnal Technologia (JIT), 17(1), 257–265. https://doi.org/10.31602/tji.v17i1.21627
Winata, A. J. (2024). Prediksi Harga Mobil Bekas Menggunakan Algoritma Gradient Boosting Machine dan Random Forest. Jurnal Inovasi Pendidikan Kreatif, 5(4), 454–464.
Yang, J., Kim, J., Ryu, H., Lee, J., & Park, C. (2024). Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models. Electronics (Switzerland), 13(12), 1–20. https://doi.org/10.3390/electronics13122345
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ida Bagus Aditya Cahya Wiraguna, Putu Indah Ciptayani, I Putu Bagus Arya Pradnyana, Ni Gusti Ayu Putu Harry Saptarini
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
