Clustering Hasil Belajar Menggunakan Algoritma K-Means Dengan Optimize Parameter Dalam Menjamin Mutu Pendidikan Di Era Pandemi Covid-19
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Ahmad Rifai
*
STMIK LIKMI, Bandung,
Indonesia
- Fatihanursari Dikananda STMIK LIKMI, Bandung, Indonesia
- Christina Juliane STMIK LIKMI, Bandung, Indonesia (*) Corresponding Author
Abstract
The COVID-19 pandemic has greatly affected the learning environment, with the excuse of stopping the spread of COVID-19 infection. Teaching and learning activities that have generally been completed on campus face-to-face now have to be transferred to distance learning. However, one of the drawbacks of implementing distance learning is that it makes students less active, so that KBM feels tiring. The purpose of this study is to classify student learning outcomes during the COVID-19 Pandemic. The method used is the Knowledge Discovery Database (KDD) using the K-Means Algorithm. The number of clusters selected is the number of clusters with the smallest Davies Bouldin Index (DBI). The results of this study obtained 3 clusters with a DBI value of 1.379 and a centroid distance of 0.342. Cluster_1 is the data group with the highest quality index, Cluster_2 is the data group with the second highest quality index, and Cluster_0 is the data group with the lowest quality index of all clusters. By knowing the clusters of PJJ learning outcomes, it will make it easier for universities to take improvement steps to improve the quality of learning in accordance with the characteristics of each existing cluster
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