Penerapan Algoritma K-Means Dalam Pengklasteran Hasil Evaluasi Akademik Mahasiswa - Fitri Safnita; Sarjon Defit; Gunadi Widi Nurcahyo (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
Several institutions that have utilized computer-based information systems for many years certainly have quite large amounts of data. The data generated and stored in a computer system is designed to be fast and accurate in both operation and administration. This data is designed for reporting and analysis that uses that data. It turns out that there is a lot of data available, with so much data we are increasingly faced with the question, "What knowledge can we gain from this data?" The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is easy to implement and run, relatively fast, easy to adapt, commonly used in practice. The parameter that must be entered when using the K-Means algorithm is the K value. The K value is generally used based on previously known information regarding how many clusters appear in This research aims to group students based on academic evaluation results. The method used to manage student academic data uses the Data Mining method with the K-Means Clustering Algorithm. The dataset processed in this research comes from the Faculty of Engineering, Informatics Engineering Study Program, Islamic University of Riau. The dataset consists of 180 student data starting from semester 1 to semester 4. The results obtained from this research are in the form of grouping students based on the achievement student cluster, there are 104 students with a percentage of 57.72%, the student cluster with potential for achievement is 62 students with a percentage of 34 .41%, the potentially problematic student cluster has 10 students with a percentage of 5.55%, and the problematic student cluster has 4 students with a percentage of 2.22%. Therefore, it is hoped that the results of this research will provide new knowledge that can be used as a source of information and function as a reference model for academic planners to monitor and predict the development of each student's academic performance.