<![CDATA[The rapid growth of the automotive industry has intensified competition in motorcycle sales. Determining the appropriate selling price is one of the key factors in improving a company's competitiveness and profitability. Various factors influence motorcycle selling prices, including brand, model, year of manufacture, engine capacity, vehicle condition, and mileage, resulting in complex data that are difficult to analyze manually. Therefore, a data mining method is needed to process historical motorcycle sales data into valuable information that supports decision-making. This study aims to analyze and classify motorcycle selling prices using the Decision Tree C4.5 algorithm based on entropy and gain ratio. The C4.5 algorithm was selected because it can generate decision rules that are easy to interpret while providing high classification accuracy for both categorical and numerical data. The research process includes data collection, data preprocessing, entropy, gain, and gain ratio calculations, decision tree construction, and classification model evaluation. The results indicate that the C4.5 algorithm is capable of identifying the most influential attributes affecting motorcycle price classification. The resulting decision tree can be used as a basis for effectively predicting motorcycle price categories. Therefore, the Decision Tree C4.5 method can assist motorcycle dealers and automotive businesses in determining appropriate sales strategies based on the characteristics of the vehicles they offer.]]>
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