<![CDATA[In the modern logistics landscape, timely package delivery has become a critical determinant of service quality and customer satisfaction. However, frequent delivery delays at the Post Office continue to pose operational challenges, largely due to multifactorial causes such as weather, distance, and scheduling inefficiencies. This study aims to identify and analyze the dominant factors influencing delivery delays using the C4.5 decision tree algorithm, a robust data mining method capable of handling categorical and continuous variables while generating interpretable decision rules. The research utilized historical delivery data from the Post Office, encompassing attributes such as weather conditions, delivery distance, order time, and package type. The analysis revealed that weather conditions had the highest information gain (0.0282430), indicating their dominant impact on delivery performance, followed by distance and package characteristics. The model successfully generated 112 decision rules that enable managers to predict and mitigate potential delays. The findings highlight the effectiveness of the C4.5 algorithm in uncovering complex patterns within operational data and its potential to support data-driven decision-making in logistics management. The implementation of this model can significantly enhance delivery reliability, operational efficiency, and customer trust, representing a strategic advancement toward digital transformation in postal services.]]>
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