Penerapan Algoritma K-Means Untuk Klasterisasi Daerah Potensi Calon Mahasiswa Baru (Studi Kasus : Universitas Budi Darma)
Keywords:
Data Mining, K-Means Clustering, Rapid Miner, Clustering of Prospective New StudentsAbstract
K-Means clustering is used to perform clustering. The K-Means method tries to group the existing data into several unique groups, with the data in one group having the same characteristics and different from the data in other groups. Therefore, this method is used to classify potential areas for prospective new students by using several criteria, such as scores on the number of cities. One of the factors for implementing the K-Means algorithm for clustering potential areas for prospective new students at Budi Darma University is to make it easier for the campus party to group them. One of the problems in clustering is the difficulty in determining potential areas for prospective new students who must determine the number of clusters to meet. There is often a problem that has too few clusters compared to the availability of the campus for potential areas for prospective new students. The process of grouping K-Means is done by determining the initial center point randomly in a group of students. To get the results of the identification of potential areas for prospective new students, namely for the 2017-2019 class, cluster 1 consists of 3 cities from 10 cities from the 2017 batch, in cluster 2 there are 4 cities for the 2018 batch, and cluster 3 with a total of 3 cities from 10 samples from the 2017-2019.References
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