Seminar Nasional Sistem Informasi (SENASIF) Fakultas Teknologi Informasi Universitas Merdeka Malang
APLIKASI K-MEANS CLUSTERING UNTUK PENGELOMPOKKAN SISWA KE DALAM KELAS BERDASARKAN NILAI AKADEMIS, JENIS KELAMIN, PERILAKU DAN NAMA SISWA DI SMA NEGERI 1 SRENGAT
Senasif 2018

Keywords

K-Means Clustering, grouping criteria, group

How to Cite

Asriningtias, S., & Sonalitha, E. (2018). APLIKASI K-MEANS CLUSTERING UNTUK PENGELOMPOKKAN SISWA KE DALAM KELAS BERDASARKAN NILAI AKADEMIS, JENIS KELAMIN, PERILAKU DAN NAMA SISWA DI SMA NEGERI 1 SRENGAT. Seminar Nasional Sistem Informasi (SENASIF), 2(1), 1179-1187. Retrieved from https://jurnalfti.unmer.ac.id/index.php/senasif/article/view/180

Abstract

SMA Negeri 1 Srengat has approximately three hundred students per batch that will
be grouped into eight classes that have been available for each level. The process of
grouping students into the classroom should take account of the appropriate
grouping procedures of school policies to obtain optimal learning outcomes. The
classification procedures include each class should consist of low achieving and
high achieving students, equal distribution of sexes of both men and women for the
whole class, equitable distribution of the students' names from az and equitable
distribution of values of behavior students. Grouping criteria that formed include
student name, student's gender, student's academic value and student's behavior
score. To assist the process of grouping the students into the classroom, it is
necessary to build an application grouping of students with the method of K-Means
Clustering as a cluster value calculator based on the distance value of student data
with the value of the cluster center. K-Means Clustering calculation results are
grouping students who have similarity criteria into one particular cluster and divide
the students into several classes in sequence in order of class in the absence of
appropriate grouping procedures.