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Dita Kurniasari
Universitas Muhammadiyah Yogyakarta
Computer Science & IT

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The Implementation of Clustering Method With K-Means Algorithm In Grouping Data of Students’ Course Scores at Universitas Muhammadiyah Yogyakarta Asroni Asroni; Dita Kurniasari; Aprilia Kurnianti
Emerging Information Science and Technology Vol 1, No 3: August 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (950.745 KB) | DOI: 10.18196/eist.v1i3.13172

Abstract

Student grades can be a reference. A large number of student grade data in a university causes data accumulation; thus, data are grouped with data mining. This study aims to classify student grade data in the second semester. Grouping student grade data was performed using the clustering method with the K-means algorithm. The research data were derived from the database of Universitas Muhammadiyah Yogyakarta. The data were students’ grades in the academic years of 2010/2011, 2011/2012, 2012/2013, 2013/2014, and 2014/2015. The analysis process was carried out using WEKA software, SQL Server 2014 Management Studio and Microsoft Excel. The clustering method could be applied to group student grade data. Clustering with K-means formed three clusters, with cluster 0 comprising 72 students, cluster 1 consisting of 190 students, and cluster 2 totaling 133 students. A cluster with the lowest average score could be used as a consideration in updating the learning methods to optimize students’ score acquisition.
Co-Authors Aprilia Kurnianti Asroni Asroni