PENERAPAN METODE CLUSTERING K-MEANS UNTUK PENGELOMPOKAN KELULUSAN MAHASISWA BERBASIS KOMPETENSI

Abstract

The difficulty often comes every year, in choosing graduation as a student by an employee
perengkrutan partners with AMIK Labuhanbatu. Competence of each student graduation taking
influence the employee in question, so as not to disappoint both sides. In this study, the authors try to
classify the data using a competency-based graduation one simple data mining techniques, namely
clustering technique (clustering) two-dimensional, which means the two variables that will be used in
grouping the graduation GPA and the value of student competencies that. Algorithms used in the
grouping (clustering) using the K-Means algorithm that starts with a random selection of K, which is
the number of clusters to be formed from the data to be in the cluster. This testing is done manually
in addition also performed with the RapidMiner data mining application of 46 records. From the
results of a study of 46 records were done manually or using RapidMiner data mining applications
have 3 groups of competency-based graduation with similar results between the two tests, namely
Cluster 1 consists of passing information to the student with IPK of 30.0 to 31.7 and 62.50 to
Competency with 71,50 the number of members of cluster 13. Cluster 2 consists of graduation students
with IPK of 30.0 to 33.5 and 81.00 to 89.00 competencies that number 18 cluster members. Cluster 3
consists of graduate students with IPK of 30.0 to 33.8 and 72.50 to 79.00 competencies that number
15 cluster members.