ANALYZING LECTURER PERFORMANCE FACTORS FROM COURSE EVALUATION SURVEYS USING K-MEANS CLUSTERING AND C4.5 CLASSIFICATION

Abstract

One of the quality of education of a college can be seen from the quality of the performance of the lecturers in the higher education Tridharma namely education, research, and development and community service. This study aims to analyze the lecturer performance factors based on the course evaluation survey on the Tridharma of higher education in the implementation of academic evaluations and decision making for lecturers in the Computer Science study program, Pakuan University. The research method applies a combination of two data mining methods, namely k-means clustering and C4.5 classification used in the assessment of the performance of lecturers, especially in the process of education and college teaching which includes pedagogical, professional, personality and social competencies. The results of the K-means clustering mining process were assessed by learning, namely 5 sufficient lecturer clusters, 16 good cluster lecturers and 14 excellent cluster lecturers. C 4.5 classification is used to see the connectedness of factors such as learning, publication, education, PKM, support. This study shows that publication criteria are the most influential factors in the performance assessment of lecturers. Testing the level of accuracy using the K-fold Cross Validation method with 5-fold Cross Validation is 80.00% and 7-fold Cross Validation which is 82.86%.