Abstract
The process of mapping the potential of prospective new students is a grouping of prospective students based on various criteria which will be grouped based on their potential, both low and high, in order to assist the PR/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. This research can provide a mapping of potential new students using the K-Means Clustering Algorithm, namely by analyzing the initial data group, transforming the initial data and performing grouping calculations, after that the results of the grouping calculations can be re-analyzed to see where the school came from, the location of the school and the location of the school. the origin of information for each member in each group. In it applied the variables, namely the origin of the school, the location of the origin of the school and the origin of the information. This is done to map the potential of prospective new students, in order to assist the public relations/promotion bureau in providing reference data and information in order to determine what promotion strategy will be carried out in the next period. A cluster validity test has been carried out using the Silhoutte Coefficiency of the K-Means algorithm which is applied with a value of 0.6113 which means that the cluster created is included in the "Medium Structure" category, it is based on the Sillhoutte Category table according to Kauffman and Roussseeuw.
References
Hermawati, F. A. (2013). Data Mining. Yogyakarta: Penerbit Andi.
Kusrini & E.T Luthfi. (2009). Algoritma Data Mining. Andi. Yogyakarta.
Moore, Andrew. (2001). K-means and Hierarchical Clustering. Pennsylvania.
Santosa, Budi. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta.
Simovici, D. A. (2012). The k -Means Clustering . In Linear Algebra Tools for Data Mining. United States of America.
Sugiyono. (2018). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Alfabeta. Bandung.
Tan, P.N., Steinbach, M., Kumar, V. (2006). Introduction to Data Mining. Pearson Education. Boston.
Wanto, Anjar. (2020). Data Mining : Algoritma dan Implementasi. Medan. Yayasan Kita Menulis.
Xindong Wu & Vipin Kumar. (2009). The Top Ten Algorithms in Data Mining. United States of America
Yunita, Fitri. (2018). Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru. Jurnal SISTEMASI, Volume 7, Nomor 3 September 2018 : 238 – 249