Implementasi Algoritma Random Forest Pada Sistem Penilaian Kinerja Dosen

Lecturer performance evaluation is a crucial process in improving the quality of education in higher education institutions. However, one of the challenges in evaluating lecturer performance is analyzing large amounts of data objectively and efficiently. This research applies the Random Forest algorithm to automatically evaluate lecturer performance data based on Employee Performance Targets (SKP). The method used is Rapid Application Development (RAD), which includes requirement planning, design, construction, and system implementation. The research results show that the application of the algorithm in the developed system can classify lecturer performance with an accuracy rate of 90% for main performance evaluation and 96.67% for behavioral performance evaluation. These results indicate that the Random Forest algorithm can be used as a tool to assist in data-based lecturer performance evaluation.