Mapping Top Grade Students Using the K-Means Clustering Algorithm
Abstract
The purpose of this study was to classify the data of superior class students so as to facilitate student education based on differences inability to learn and participate in learning at school. Grouping students with more and more data becomes less effective and efficient. Moreover, grouping students who have achievements to enter the superior class, of course, requires a computer-based system. Every year the number of students increases, thus the number of student data also continues to increase and there is a process of data accumulation that is difficult to process optimally. Conventional decision-making, cannot handle very large amounts of data. For this reason, data mining is needed in order to solve these problems through patterns of data accumulation. The application of k-means with a sample of 120 data from student report cards of SMK Raksana 2 Medan, can accelerate the grouping of superior class students by inputting the centroid value and finding the closest value so as to produce 3 clusters, namely: cluster 1 mastery of android programming as many as 10 students, cluster 2 mastery web programming as many as 62 students and cluster 3 mastering desktop programming as many as 48 students. The application of data mining with a web-based k-means algorithm to process it into information and knowledge as consideration for educators in the decision-making process.
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