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Asri Samsiar Ilmananda
Sistem Informasi, Universitas Merdeka Malang, Indonesia
Education

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Penggunaan Algoritma K-Means dan K-Medoids untuk Pengembangan Strategi Promosi Penerimaan Mahasiswa Baru Devita Maulina Putri; Asri Samsiar Ilmananda; Nadita Prisanta
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1474

Abstract

New student admission is a crucial activity for universities, especially private universities, in obtaining new students. FTI Unmer Malang has implemented various promotional techniques, but still experiences obstacles in achieving student admission targets. The number of new students fluctuates, with a peak in admission in 2019 and a significant decrease of 23% in the last three years. One of the main problems is the lack of information dissemination to remote areas. To overcome this problem, this study applies a data mining method with clustering to group new student data based on their area of ​​origin. Two clustering algorithms, namely K-Means and K-Medoids, are used to compare clustering results to find the optimal promotion strategy. The data used includes new students from the 2016 to 2022 academic years. The results of the study show that the K-Means algorithm shows better performance than the K-Means algorithm with DBI index accuracy level of 0.344. The results of the study are expected to help FTI Unmer Malang in determining a more effective promotion strategy based on the student's area of ​​origin.
Co-Authors Devita Maulina Putri, Devita Maulina Nadita Prisanta