<![CDATA[The implementation of conventional work permit management systems often causes problems, such as delays in approval, the risk of document loss, and limitations in real-time monitoring of permit status. This study developed a web-based work permit management system that automates administrative processes and supports managerial decision-making through historical data analysis. The system was developed using the Waterfall method and implemented with a client–server architecture, while the Support Vector Machine (SVM) algorithm with Radial Basis Function (RBF) kernel was applied to classify permit patterns. Test results show that the SVM model is capable of achieving 99.33% accuracy in testing data classification. The findings of this study indicate that the integration of web-based systems with predictive algorithms can improve the efficiency of the licensing process while providing a scientific framework for analyzing labor licensing patterns, thereby contributing to the development of data-based human resource management methods.]]>
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