Pengelompokan Mahasiswa untuk Pengajuan Bantuan Uang Kuliah Tunggal menggunakan Metode KÂMeans Clustering (Studi Kasus BEM FILKOM UB) - Nourman Hajar; Nanang Yudi Setiawan; Fitra Abdurrachman Bachtiar (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
University of Brawijaya (UB) implements single tuition fee (UKT) system based on the students' economic background. Students at FILKOM UB who have difficulties to pay for UKT can apply for UKT aid to the faculty through Advokesma BEM FILKOM who will do selection for the submissions. The large number of data that must be verified in a short time span makes it difficult to select the submission to be recommended. A data clustering system is needed maket UKT aid submission selection easier. Clustering is done using K-Means algorithm using economic condition data of active students in FILKOM UB who enrolled via SNMPTN and SBMPTN. The number of clusters produced is six, based on the number of UKT groups in UB.. Validation test using Calinski-Harabasz index shows that six is the most optimal number of clusters with a score of 74.84. The KÂMeans algorithm groups student data into UKT groupsfor later analysis. The presentation of the results of clustering uses Google Data Studio in the form of a dashboard that presents information on the distribution of data and the results of clustering. Usability testing using System Usability Scale (SUS) produces score of 72.5 which indicates the dashboard is acceptable with a good rating. From the data analysis, Advokesma BEM FILKOM can determine priorities in recommending the UKT aid submission based on the comparison of the student's clustered group and the student's UKT group..