Algoritma K-Means Clustering Sebagai Penentuan Beasiswa PIP pada SMPN 9 Kota Blitar

Abstract

The structure in this study involves taking data on obtaining PIP scholarships with various predetermined indicators (parental income, parents' occupation, vehicle ownership, KIP, KKS, place of residence, number of siblings, PKH) as scholarship recipients. Held in one of the state schools SMPN 9 Blitar City. Where the data is processed with an unsupervised learning algorithm, namely K-Means Clustering with the calculation of the Euclidean distance with the closest value distance from the predetermined centroid which is then used to determine the recipient of the PIP scholarship. The sample test was conducted on 289 students which could be implemented properly and the cluster was running according to the provisions set as indicators of 10 attributes (X1-X10).