Analysis of Electric Vehicle Clustering and Price Prediction Using K-Means Clustering and Multiple Linear Regression
DOI:
https://doi.org/10.25181/rt.v4i2.4975Keywords:
K-Means Clustering, Cross-Validation, Electric Vehicles, Multiple Linear Regression, Evaluation MetricsAbstract
The development of the electric vehicle industry has increased the need for accurate and data-driven vehicle price prediction systems. This study aimed to analyze clustering and predict electric vehicle prices based on vehicle technical characteristics using K-Means Clustering and multiple linear regression methods. The dataset consisted of 2,000 observations with variables including vehicle price, production year, battery capacity, driving range, and charging speed. The optimal number of clusters was determined using the Elbow Method, followed by the implementation of K-Means Clustering with three clusters. The clustering results were then used as additional variables in the multiple linear regression model evaluated using 10-fold cross-validation. The results showed that the addition of cluster variables improved prediction model performance, indicated by a decrease in RMSE from 33,366.41 to 29,981.22 and MAE from 26,211.76 to 23,508.42, as well as an increase in R² from 0.1092 to 0.2827. These findings indicate that the combination of K-Means Clustering and multiple linear regression can improve the model’s ability to predict electric vehicle prices based on vehicle characteristics.
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Copyright (c) 2026 Kharla Septiana, Afni Cahaya Putri, Nadya Monalisa, Dewi Astuti, Etis Sunandi, Wina Ayu Lestari
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