Implementasi Algoritma K-Means untuk Klasterisasi Kinerja Akademik Mahasiswa - Fajar Nur Rohmat Fauzan Jaya Aziz; Budi Darma Setiawan; Issa Arwani (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Selection of student acceptance in a college produces abundant data and can be utilized to obtain useful information for the college. In this study, student data taken by the authors are Student ID Number, University Entrance Path, Parent Revenue and Student Achievement Index. Excavation of information on a large data could not be done easily and this can be done with data mining technology. Data mining also known as Knowledge Discovery in Database is an automated process of searching data in a very large memory of data to know patterns by using tools such as association or clustering. By using k-means clustering method, the researcher tries to extract the knowledge which can depict the performance of student achievement at the end of semester and the result of the research indicates that of all cluster quantities inserted, for clusters amounting to 3 (three) has the value of silhouette coefficient closest to the value of = 1, that is with the value of 0.108690751. In addition, parental income does not affect the level of academic performance of students and the academic value of students who enter through the regular path & achievement paths have the value of the highest average GPA. Thus, the faculty can consider to prioritize the acceptance of new students through regular channels & achievement contract.