Forecasting Used Car Prices Using Machine Learning - Khotimah, Eni Khusnul; Swasono, Dwiretno Istiyadi; Fajarianto, Gama Wisnu (IT Journal Research and Development, 2025)
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.