Pengelompakan Hasil Survei Merdeka Belajar Kampus Merdeka Di Universitas Bhayangkara Jakarta Raya Menggunakan Kmean Dan K-Medoids Clustering

Abstract

The goal of this study is to categorize the findings of a survey on the application of the MBKM policy that DIKTI performed via universities that had been awarded research funding. The survey results have not been categorized, making it difficult for the institution to determine if the MBKM policy has been implemented in accordance with the MBKM standards released by the Higher Education. The K-Mean and K-Medoids Algorithms are used in this study technique to solve data grouping issues and validate clustering outcomes using the Davies-Bouldin Index (DBI). 400 data points total were processed from 16 variables in this investigation. The findings of this investigation were tested using several clusters. After analyzing clusters using DBI, the K-Mean algorithm discovered that cluster 5 had K-Medoids of 0.9 and a value of 0.823. Therefore, it is advised to employ 5 clusters with the K-Mean Algorithm for grouping data from the MBKM survey findings.