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 (Universitas Muhammadiyah Yogyakarta, 2020)
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.