Penerapan Algoritma K-Means Clustering pada Data Nilai Siswa untuk Menentukan Kelompok Penerima Beasiswa - Sindrawati Sindrawati; Dodi Syaripudin; Agung Rachmat Raharja (SisInfo, 2024)
To prevent school mistakes in determining class XII students who are entitled to receive scholarships, prevention can be done by using data mining techniques so that the school can determine decisions accurately and quickly. Clustering is a data mining technique that functions to group a number of data or objects into clusters (groups) so that each cluster will contain data that is as similar as possible and different from objects in other clusters. The method used is CRISP-DM, through the business understanding process, data understanding, data modeling, deployment, assessment, and preparation. The K-Means algorithm is the one used to construct clusters. K-Means is a non-hierarchical technique for clustering data that divides student data into multiple clusters according to how similar the data is. A total of 109 data points were used, including majors and grades in English, Indonesian, and mathematics. The three clusters that were created are as follows: the first cluster has 26 pupils, the second has 46 kids, and the third has 37 students. Based on the clusters that were generated, the study's findings can be utilized to inform decisions about which scholarship recipients should be grouped..