<![CDATA[Lecturer performance evaluation is a crucial process in improving the quality of education in higher education institutions. However, one of the main challenges in evaluating lecturer performance is analyzing large amounts of data objectively and efficiently. This study applies the Random Forest algorithm to automatically evaluate lecturer performance data based on the Employee Performance Target (SKP). The method used is Rapid Application Development (RAD), which includes requirements planning, design, construction, and system implementation. The results of the study show that the implementation of the algorithm in the developed system is capable of classifying lecturer performance with an accuracy rate of 72.73% for main performance assessment and 81.82% for behavioral performance. These results indicate that the Random Forest algorithm can be used as a supporting tool in data-driven lecturer performance evaluation.Lecturer performance evaluation is a crucial process in improving the quality of education in higher education institutions. However, one of the main challenges in evaluating lecturer performance is analyzing large amounts of data objectively and efficiently. This study applies the Random Forest algorithm to automatically evaluate lecturer performance data based on the Employee Performance Target (SKP). The method used is Rapid Application Development (RAD), which includes requirements planning, design, construction, and system implementation. The results of the study show that the implementation of the algorithm in the developed system is capable of classifying lecturer performance with an accuracy rate of 72.73% for main performance assessment and 81.82% for behavioral performance. These results indicate that the Random Forest algorithm can be used as a supporting tool in data-driven lecturer performance evaluation.]]>
