Open AccessAnalisis Data Mining Pesebaran Siswa Smp Di Pematangsiantar Dengan Metode Algoritma K-Means Clustering
Author(s) -
Kurnia Sandi Purba,
Dedy Hartama,
Sitti Suhada
Publication year - 2022
Publication title -
kesatria
Language(s) - English
Resource type - Journals
ISSN - 2720-992X
DOI - 10.30645/kesatria.v3i1.91
Subject(s) - cluster analysis , computer science , class (philosophy) , service (business) , selection (genetic algorithm) , data mining , mathematics education , machine learning , artificial intelligence , mathematics , economics , economy
Data Mining is an automatic analysis of large or complex data with the aim of finding important patterns or trends that are usually not realized. However, the student data has not been utilized optimally, making it difficult for the school to carry out student development at school. Each student will make a comparison between the desired service with the service received. The K-Means algorithm is an iterative cluster technique algorithm. This algorithm starts with random selection, which is the number of clusters that you want to form. The attributes needed in processing are the student's hometown, the student's school origin, and the student's grade. Based on these attributes, the data will then be grouped into junior high school students based on city origin, school origin, and student grades using the K-Means Clustering algorithm. The results of processing this data will greatly help the junior high school, so that it is known that the distribution of each junior high school student is the most. In this study, the data used were data from junior high school students from the 2018-2019 class in Pematangsiantar as many as 300 data samples