Research Article
Recommendation of Student Admission Priorities Using K-Means Clustering
@INPROCEEDINGS{10.4108/eai.2-5-2019.2284614, author={Hidayatul Muttaqien and Muhammad Lutfi and Musliadi KH and Abdul Muis and Hazriani Zainuddin}, title={Recommendation of Student Admission Priorities Using K-Means Clustering}, proceedings={1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia}, publisher={EAI}, proceedings_a={ICOST}, year={2019}, month={6}, keywords={recommendation k-means clustering priorities centroid}, doi={10.4108/eai.2-5-2019.2284614} }- Hidayatul Muttaqien
Muhammad Lutfi
Musliadi KH
Abdul Muis
Hazriani Zainuddin
Year: 2019
Recommendation of Student Admission Priorities Using K-Means Clustering
ICOST
EAI
DOI: 10.4108/eai.2-5-2019.2284614
Abstract
This study aims to investigate student’s characteristics based on three variables, namely grade point average (GPA), period of study, and administrative obedience in order to draw a recommendation for student admission priorities at Mulawarman University. This recommendation will be used as one of reference variable on new student recruitment. The 8.741 records of student data sourced from the university data warehouse were mined using K-Means clustering. This mining process produced three clusters, cluster-1 includes 1,758 students with centroid {0.158,0.694,0.663}, while cluster-2 embraces 4,928 students with centroid {0.970,0.700,0.675}, and cluster-3 with centroid {0.953,0554,0.386} covers 2.055 students. This result shows that cluster-2 has the best combination of centroid values, implied that new students from schools where students in cluster-2 graduated from are recommended as the high priority students to be admitted at Mulawarman University