Klustering Data Mahasiswa Menggunakan Metode K-Means Sebagai Acuan dalam Penentuan Uang Kuliah Tunggal (UKT) Mahasiswa
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Abstract
Determining Uang Kuliah Tunggal/UKT for new students is important in Penerimaan Mahasiswa Baru/PMB process after PMB selection process. The determination of UKT groups by The PMB committee at Politeknik Negeri Cilacap is carried out one by one by looking at the economic data of new students. This condition has become a special problem due to the increase in PMB quotas in the PNC, so it requires alternative solutions that can be used as one of the benchmarks in the determination of a new student UKT group in PNC. The researchers used clustering with features that represent the economic conditions of new students with the K-means method to provide alternative solutions. The result of using the K-Means method in clustering, yielding a performance value for the number of clusters 8 of 1669,283, with the highest number of cluster members in cluster members in cluster 4 being 72 out of 275 data. The Elbow method test results to determine the best number of clusters resulting in 4 cluster with a performance value of 2462,003.
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