<![CDATA[Penelitian This study discusses the design of a web-based operational vehicle management information system at UD Sentosa utilizing a data mining-based quantitative approach. The background of this research is driven by the fact that the operational vehicle management process is still conducted manually, leading to various issues such as difficulties in vehicle data logging, borrowing, maintenance, and vehicle operational reporting. These conditions potentially cause information delays, low data accuracy, and suboptimal vehicle maintenance decision-making. The research methodology employed is applied research with a quantitative approach. Data analysis was conducted using data mining methods by implementing the and Decision Tree algorithms to classify operational vehicle maintenance conditions based on historical vehicle data. The research demonstrates that the developed web-based operational vehicle management information system is capable of assisting the processes of vehicle data logging, borrowing, maintenance, and reporting more effectively and efficiently. In addition, the implementation of the and Decision Tree algorithms successfully provides classification information regarding vehicle maintenance conditions, which can be utilized as a support for decision-making. Consequently, the constructed system is expected to improve the effectiveness and quality of operational vehicle management at UD Sentosa in an integrated and data-driven manner.]]>
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