Implementasi Algoritma K-Means Clustering Dalam Penilaian Kedisiplinan Siswa - Qorik Indah Mawarni; Eko Setia Budi (Jurnal Sistem Komputer dan Informatika (JSON), 2022)
Education has a very important role for students, not just potential but noble character in the form of discipline, therefore it is necessary to group each school based on student discipline. Implementing a clustering system with the K-means method which is used to classify and determine the value of student discipline which produces a clustering output of student discipline that is beneficial for the school to prevent students from misbehaving early on. Analysis of data needs used in this study in the form of primary data obtained from a questionnaire given to students. The attributes used are presence, neatness and behavior. Student discipline assessment can be carried out using the k-means clustering method. This study applies the K-Means clustering algorithm method using Microsoft Excel 2013 and Orange which performs the data mining process. The results of the research implementation of the k-means clustering algorithm in student disciplines are divided into three clusters. From a sample of 133 students, 41 students were included in cluster one (C1), then 33 students were included in the second cluster (C2), and 59 students were included in cluster three (C3). The results of grouping the level of student discipline using the k-means method can be used as a reference or assessment of the discipline of each student.