<![CDATA[The increasing volume of used motorcycle transactions has led to the rapid growth of vehicle-related data, providing valuable opportunities to support more objective vehicle price classification. However, in practice, the pricing of used motorcycles is often influenced by subjective assessments, resulting in prices that may not accurately reflect the actual condition of the vehicles. This study aims to apply the C4.5 algorithm to classify used motorcycle prices based on vehicle characteristics contained in the motor_second.csv dataset. The research methodology consists of data collection, data cleaning, data transformation, dataset partitioning into training and testing sets, Decision Tree model construction, and model evaluation using a confusion matrix, classification accuracy, and feature importance analysis. The results demonstrate that the C4.5 algorithm successfully generates an interpretable decision tree capable of explaining the relationship between vehicle attributes and price categories. In addition to producing a classification model, the proposed approach also generates decision rules that can serve as practical guidelines for estimating used motorcycle price categories. Therefore, the C4.5 algorithm can be effectively utilized as a decision support method for the classification of used vehicle prices.]]>
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