DATA CLUSTERING ON TEACHING AND LEARNING EVALUATION APPLICATION OVER PANDEMIC ERA

SIEPEL is an evaluation application for teaching and learning process in University of Bengkulu. It mandatory every student to fill the questionnaire before they can see marking value each subject semester. This survey was designed meet requirements expectations students as educational service customers. data very important improve quality further policy decision maker. However, analysis remains open question size distribution become some issues analysis. Here, we showed new approach using K-Means Clustering better understanding over data. paper used elbow method find best number clusters be implemented on algorithm which results four satisfaction values (unsatisfied, less satisfied, satisfied). The result this published based website system show visualization Furthermore, research that average 4 semester satisfied 6.50%; 43.89%; 44.26%; not 5.36%. vary dropped from 20.47% 0.12% by 2 years increased 27.64% 66.32%. term happened because pandemic era change