APPLICATION OF THE K-MEANS METHOD FOR PREPARATION OF NEW STUDENTS ENTRY SELECTION
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Nov 9, 2022
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Abstract
The moment of acceptance of new students is often an event for schools to get potential students, especially in the academic field. This is done because the good name of the school is strongly influenced by the achievements of its students either through competitions, art events or other activities. But on the other hand, many prospective new students do not understand what potential is in them. For that, we need a system that is able to classify students based on student potential. The grouping uses the National Examination scores as the basis for this grouping. The clustering method used is the K-Means Clustering method. The data is divided into two clusters which are divided into general school groups and madrasah school groups. From the results of clustering, it was found that 6 students were in the general school group and 11 students in the madrasa school group.
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Fuad, N., Edy Sudiati, L., & Haryani, N. (2022). APPLICATION OF THE K-MEANS METHOD FOR PREPARATION OF NEW STUDENTS ENTRY SELECTION. SOSCIED, 5(2), 223-229. https://doi.org/10.32531/jsoscied.v5i2.527
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
LPPM Politeknik Katolik Saint Paul Sorong
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Battaglia O R, Paola B D, dan Fazio C.(2017). K-means Clustering to Study How Student Reasoning Lines Can Be Modified by a Learning Activity Based on Feynman’s Unifying Approach. EURASIA Journal of Mathematics Science and Technology Education. 13:6 2005-2038.
Veluchamy R, Bera S, Rajput A S, dan Krishnan A R.( 2016 ). Students’ Talent Management in Creating Student CEOs. International Journal of Control Theory and Applications. 9:37 685-692.
Aggarwal D, dan Sharma D. (2019). Application of Clustering for Student Result Analysis. International Journal of Recent Technology and Engineering. 7:6C 50-53.
Chang W, Ji X, Liu Y, Xiao Y, Chen B, Liu H, dan Zhou S. (2020). Analysis of University Students’ Behavior Based on a Fusion K-Means Clustering Algorithm. Applied Sciences. 10 6566.
Muttaqien H, Lutfi M, KH Musliadi, Muis A, dan Zainuddin H. (2019). Recommendation of Student Admission Priorities Using K-Means Clustering. EAI Publisher: 1st ICOST Proceeding. DOI 10.4108/eai.2-5-2019.2284614.
Sya’iyah K, Yuliansyah H, dan Arfiani I. (2019). Clustering Student Data Based On K-Means Algorithms. International Journal of Scientific & Technology Research. 8:08 1014-1018.
Chunsheng L, Kejia Z, Tao L, dan Yanan H.( 2020). Analysis of Postgraduate Admission Scores Based on K-means Algorithm. IEEE Publisher: 2020 ICBDIE, 402-406. doi: 10.1109/ICBDIE50010.2020.00101.
Kurniawan C, Setyosari P, Kamdi W, dan Ulfa S. (2018). Electrical Engineering Student Learning Preferences Modelled Using K-Means Clustering. Global Journal of Engineering Education. 20:2 140-145.
Sari H L, Suranti D, Zulita L N.(2017). Implementation of K-Means Clustering Method for Electronic Learning Model. IOP Conference Series: Journal of Physics: Conference Series. 930 012021.
Purba W, Tamba S, dan Saragih J.(2018). The Effect of Mining Data K-Means Clustering Toward Students Profile Model Drop Out Potential. IOP Conference Series: Journal of Physics: Conference Series. 1007 012049.