Forecasting Used Car Prices Using Machine Learning
DOI:
https://doi.org/10.25299/itjrd.2025.18031Keywords:
Car price prediction, Machine Learning, Artificial Neural Network, Random Forest Regression, Mean Absolute Error (MAE)Abstract
In an increasingly competitive era, it is crucial for car dealers and retailers to address the challenges of accurately determining the prices of used cars. To tackle these challenges, this study implements Machine Learning models to predict used car prices accurately. By applying the Artificial Neural Network (ANN) and Random Forest Regression algorithms, this research aims to evaluate the performance of these methods in predicting used car prices. The used car price data was obtained from the Kaggle repository, consisting of 14,657 data entries that provide comprehensive information about used cars. The analysis focuses on six main columns, including Brand, Model, Variant, Year, and Mileage, to estimate used car prices. Model evaluation was conducted using Mean Absolute Error (MAE) as the primary metric. The results show that the ANN model achieved a lower MAE (0.035) compared to the Random Forest Regression (0.047), indicating better performance in predicting used car prices. These findings demonstrate the effectiveness of ANN in handling data complexity and the non-linear relationships between variables involved in forecasting used car prices. Additionally, this contributes to the implementation of more accurate used car price predictions, enabling automotive companies to improve operational efficiency and provide greater benefits to the community.
Downloads
References
[1] E. Surya Negara, J. Jenderal Ahmad Yani, K. I. Seberang Ulu, and S. Selatan, “Sulaiman et al, Komparasi Algoritma K-Nearest Neoghbors dan Random Forest …….. 337 Komparasi Algoritma K-Nearest Neighbors dan Random Forest Pada Prediksi Harga Mobil Bekas,” 2023. [Online]. Available: www.cardekho.com.
[2] S. Laxmaiah, K. Shireesha, and B. Prathima, “Prediction of Used Car Prices Using Artificial Neural Networks and Machine Learning”, doi: 10.32628/IJSRCSEIT.
[3] C. Tarmanini, N. Sarma, C. Gezegin, and O. Ozgonenel, “Short term load forecasting based on ARIMA and ANN approaches,” Energy Reports, vol. 9, pp. 550–557, May 2023, doi: 10.1016/j.egyr.2023.01.060.
[4] V. Yadav, A. K. Yadav, V. Singh, and T. Singh, “Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review,” Jun. 01, 2024, Elsevier B.V. doi: 10.1016/j.rineng.2024.102305.
[5] G. Toğa, B. Atalay, and M. D. Toksari, “COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey,” J Infect Public Health, vol. 14, no. 7, pp. 811–816, Jul. 2021, doi: 10.1016/j.jiph.2021.04.015.
[6] S. Lessmann and S. Voß, “Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy,” Int J Forecast, vol. 33, no. 4, pp. 864–877, Oct. 2017, doi: 10.1016/j.ijforecast.2017.04.003.
[7] B. K. Meher, M. Singh, R. Birau, and A. Anand, “Forecasting Stock Prices of Fintech Companies of India using Random Forest with High-frequency Data,” Journal of Open Innovation: Technology, Market, and Complexity, p. 100180, Mar. 2023, doi: 10.1016/j.joitmc.2023.100180.
[8] M. Mehdi Karakoç, G. Çelik, and A. Varol, “Car Price Prediction Using An Artificial Neural Network,” 2020.
[9] B. Kriswantara, K. Kurniawati, and H. F. Pardede, “Prediksi Harga Mobil Bekas dengan Machine Learning,” Syntax Literate ; Jurnal Ilmiah Indonesia, vol. 6, no. 5, p. 2100, May 2021, doi: 10.36418/syntax-literate.v6i5.2716.
[10] A. Lusiana and P. Yuliarty, “PENERAPAN METODE PERAMALAN (FORECASTING) PADA PERMINTAAN ATAP di PT X.”
[11] L. Purwati Ayuningtias and M. Irfan, “Analisa Perbandingan Logic Fuzzy Metode Tsukamoto, Sugeno, Dan Mamdani (Studi Kasus : Prediksi Jumlah Pendaftar Mahasiswa Baru Fakultas Sains Dan Teknologi Universitas Islam Negeri Sunan Gunung Djati Bandung),” 2017.
[12] E. R. Habibi, “peramalan harga garam konsumsi menggunakan artificial neural network feedforward-backpropagation (studi kasus : pt. Garam mas, rembang, jawa tengah),” 2017.
[13] Gupta G, “Artificial Intelligence and Expert Systems. In Mercury Learning & Information. Mercury Learning & Information.,” 2020.
[14] A. Al Miaari and H. M. Ali, “Batteries temperature prediction and thermal management using machine learning: An overview,” Nov. 01, 2023, Elsevier Ltd. doi: 10.1016/j.egyr.2023.08.043.
[15] K. P. P. J. K. P. S. Shikha Jain, Artificial Intelligence, Machine Learning, and Mental Health in Pandemics A Computational Approach. Academic Press. 2022.
[16] B. Kriswantara and R. Sadikin, “Used Car Price Prediction with Random Forest Regressor Model,” Journal of Information Systems, Informatics and Computing Issue Period, vol. 6, no. 1, pp. 40–49, 2022, doi: 10.52362/jisicom.v6i1.752.
[17] S. Dasariraju, M. Huo, and S. McCalla, “Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm,” Bioengineering, vol. 7, no. 4, p. 120, Oct. 2020, doi: 10.3390/bioengineering7040120.
[18] S. Asadi, S. Roshan, and M. W. Kattan, “Random forest swarm optimization-based for heart diseases diagnosis,” J Biomed Inform, vol. 115, p. 103690, Mar. 2021, doi: 10.1016/j.jbi.2021.103690.
[19] F. R. Aszhari, Z. Rustam, F. Subroto, and A. S. Semendawai, “Classification of thalassemia data using random forest algorithm,” J Phys Conf Ser, vol. 1490, no. 1, p. 012050, Mar. 2020, doi: 10.1088/1742-6596/1490/1/012050.
[20] N. Abdulkareem and A. Abdulazeez, “Machine Learning Classification Based on Radom Forest Algorithm: A Review,” 2021, doi: 10.5281/zenodo.4471118.
[21] P. Wibowo and C. Fatichah, “An in-depth performance analysis of the oversampling techniques for high-class imbalanced dataset,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 7, no. 1, pp. 63–71, 2021, doi: 10.26594/register.v7i1.2206.
[22] Y. Liu, G. Yan, and A. Settanni, “Forecasting the transportation energy demand with the help of optimization artificial neural network using an improved red fox optimizer (IRFO),” Heliyon, vol. 9, no. 11, Nov. 2023, doi: 10.1016/j.heliyon.2023.e21599.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Eni Khusnul Khotimah, Dwiretno Istiyadi Swasono, Gama Wisnu Fajarianto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. The copyright in the text of individual articles (including research articles, opinion articles, and abstracts) is the property of their respective authors, subject to a Creative Commons CC-BY-SA licence granted to all others. ITJRD allows the author(s) to hold the copyright without restrictions and allows the author to retain publishing rights without restrictions.
