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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Clustering Student Data Based On K-Means Algorithms

[Full Text]



Khoiriyatus Sya’iyah, Herman Yuliansyah, Ika Arfiani



Data Mining, Clustering, K-Means Algorithms, Student Data, Student Performance, Educational.



Educational data mining is interesting research always to discuss. Student data has the potential to be further processed and provide results for other uses. By grouping student data, the educational institution will get useful potential knowledge. The methodology in this research divided into five steps, i.e., data cleaning, data selection, data transformation, clustering using K-Means Algorithms, and knowledge presentation. We split the cluster of student data into three groups. It is because we want to get characteristic of the student with excellent performance, standard performance, and underperformance. We use 724 student data and four variables, i.e., Grade Point Average (GPA), length of study (LS), English proficiency score (EP), and length of thesis working (LT). The results of this research are the three characteristics of the student, i.e., the students in cluster 1 have 3.28 scale 4 for GPA, 4.52 years for LS, scores 404 for EP, and 7.46 month for LT. The students in cluster 2 have 3.29 scale 4 for GPA, 4.48 years for LS, scores 481 for EP, and 7.26 month for LT. The students in cluster 3 have 3.31 scale 4 for GPA, 4.50 years for LS, scores 437 for EP, and 7.14 month for LT.



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