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Daniel Tambun Daniel
Univesitas Pelita Bangsa
Computer Science & IT

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Pengelompokan Penerimaan Mahasiswa Baru Dengan Algoritma K-Means Untuk Meningkatkan Potensi Pemasaran Daniel Tambun Daniel; Sifa Fauziah; Muhtadhuddin Danny
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.732

Abstract

Utilization of the existing PMB dataset through the clustering method approach can be applied in analyzing the rate of acceptance of new students. The K-Medoid Cluster algorithm model that is applied has results that show a new insight, namely the grouping of new student acceptance rates based on 3 clusters, cluster 1 (C0) is a high level consisting of 49 data from 86 datasets tested and cluster 2 (C1) is a low level consisting of 11 data from 86 datasets tested and cluster 3 (C2) is a medium level consisting of 26 data from 86 datasets tested. The results of the Davies Bouldin Index or DBI value are based on the RapidMiner Studio application obtained from data testing, with a Davies-Bouldin Index evaluation value of 0.769. Keywords: Data Mining, K-Medoid Cluster, Klastrer, PMB
Co-Authors Muhtadhuddin Danny Sifa Fauziah