Pengelompokan Mahasiswa Potensial Drop Out Menggunakan Metode Clustering K-Means - Ieannoal Vhallah; Sumijan Sumijan; Julius Santony (Ikatan Ahli Informatika Indonesia (IAII), 2018)
Clustering K Mean is used for grouping. The K-Means method seeks to group the existing data into several unique groups, where data in one group have the same characteristics with each other and have different characteristics than the data exists in the other group. To perform student grouping the potential drop out required attributes. Total Semester Credit System, Comunative Achievement Index, and Total Semester. Clustering process K- Mean is done by determining the nearest initial centroid point in a group of potential drop out students. Clustering results K-Mean by Total Credit System semester, Comunative Achievement Index, and Total Semester. Results Clustering of potential drop out students for class of 2014 is in cluster 0 of 4 students or 30.77% of 13 Samples, class of 2015 is in cluster 1 amounted to 4 students and cluster 2 amounted to 2 students or 66.7% of 9 samples , the force of 2016 is in cluster 0 amounting to 2 students and cluster 1 is 10 students or 50% from 24 samples, and force of 2017 is in cluster 2 strength 4 student or 22,22% from 18 Keywords: Data Mining, Clustering, K-Mean, Potensial Drop Out,,