Penerapan K-Means Clustering pada Penentuan Jenis Pembelajaran di Universitas Pasir Pengaraian

Abstract

The Covid-19 pandemic that has hit the world has changed the pattern of human life, including in the process of teaching and learning activities in universities. One of the universities affected by this pandemic is Pasir Pengaraian University. Lectures carried out by Pasir Pengaraian University during the Covid-19 pandemic consist of at least three forms, namely offline, online and blended learning. Efforts to assess which learning method is the most effective become important to measure the level of success of the teaching and learning process, so this study aims to determine the lecture strategy at Pasir Pengaraian University using the K-Means Clustering method. K-Means Clustering algorithm is a method in data mining that can be used for data grouping. CRISP-DM is a data mining methodology used in this study. The research dataset was obtained from the Even semester 2020 lecturer learning reports. RapidMiner was used as a tool to process the data. Clusters were formed as many as 3 (three) with the results of Cluster 1 (49 lecturers), Cluster 2 (17 lecturers), and Cluster 3 (54 lecturers). Based on these results, the lecture strategy with the Blended Learning type of learning is the most appropriate choice to be used at Pasir Pengaraian University, because apart from this Cluster having the highest number of memberships, in this Cluster the highest percentage of places to study are Classrooms/Labors and Meeting Applications, namely blend of offline and online lectures. The blended learning strategy has proven to be representative for use during the pandemic. Evaluation using DBI or Davies-Bouldin Index. The DBI value obtained is -1.163. Cluster evaluation is not good when viewed at this value, because it is negative and not close to zero