Application of K-Means Cluster Algorithm to Determine Student Achievement

Abstract

The application of the K-Means Algorithm by dividing the data into one or more groups, with data included in one group representing similarities and other groups representing differences. To assess whether or not student motivation is superior, the data from the student achievement evaluation shows the average value of each topic. Analysis, design, coding, and system testing are all included in the research steps. Evaluation is carried out to be used as a basis for the characteristics used in the calculation to ensure higher values. Information systems can achieve successful clustering classification results by including the k-means clustering method. This technique rotates the centroid distance at each iteration, forming cluster points and reducing clustering time