DATA CLUSTERING ON TEACHING AND LEARNING EVALUATION APPLICATION OVER PANDEMIC ERA

Abstract

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