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, 2024)
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.