STUDI KOMPARASI ALGORITMA K-MEANS CLUSTERING DAN K-MEDOIDS CLUSTERING DALAM PENGKLASTERAN DATA MAHASISWA

QOMARIYAH, NIM. 17106050005 (2021) STUDI KOMPARASI ALGORITMA K-MEANS CLUSTERING DAN K-MEDOIDS CLUSTERING DALAM PENGKLASTERAN DATA MAHASISWA. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Universities as educational institutions have very large amounts of academic data and student administrative data, which may not be used properly. Student academic data that has accumulated from year to year needs to be analyzed to produce information can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index (DBI). The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. Based on the research that has been done on the clustering process, the number of clusters 2,3,4,5, and 6. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test that the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students

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