Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination

  • Muhammad Al Ghifari Informatics Engineering Department, Paramadina University
  • Wahyuningdiah Trisari Harsanti Putri Informatics Engineering Department, Paramadina University
https://doi.org/10.25139/inform.v8i1.4519
Abstract views: 279 , PDF downloads: 211
Keywords: K-Means, Elbow Method, Student Course, Clustering, Data Mining

Abstract

Students who have taken courses will receive grades from a performance index with a weight of 0 to 4. The amount of historical student data, particularly on course grades, has the potential to discover new insights. Still, course grades are closed data and are only for academic and management purposes. The research aims to a grouping of courses with high average grades. In this research, the clustering of courses using the k-means clustering algorithm using the elbow method to determine the centroid. Based on the Sum of Squares calculation, the optimal number of clusters with k=2 was obtained. The clustering results produced cluster 1 with a centroid value of 2.686 and 15 members and cluster 2 with a centroid value of 3.245 and 40 members. It can be concluded from this research that the members of cluster 2 are a group of courses with high average grades.

 

Author Biographies

Muhammad Al Ghifari, Informatics Engineering Department, Paramadina University

 

 

Wahyuningdiah Trisari Harsanti Putri, Informatics Engineering Department, Paramadina University

 

 

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Published
2023-01-27
How to Cite
Al Ghifari, M., & Harsanti Putri, W. T. (2023). Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 8(1), 42-46. https://doi.org/10.25139/inform.v8i1.4519
Issue
Vol. 8 No. 1 (2023)
Section
Articles

Copyright (c) 2023 Muhammad Al Ghifari, Wahyuningdiah Trisari Harsanti Putri

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