Implementasi Algoritma K-Means Clustering Seleksi Siswa Berprestasi Berdasarkan Keaktifan dalam Proses Pembelajaran
DOI:
https://doi.org/10.14421/jiska.2022.7.2.111-121Keywords:
Data Mining, Keaktifan Siswa, K-Means, Prediksi Clustering, Davies BouldinAbstract
The learning process through various interactions and learning experiences has a considerable influence on developing student activity to improve the quality of education. The teacher is the most important factor in determining the success of students in the implementation of the process. The development of the quality and activeness of students in learning is a basic element as a form of success in the learning process which of course not all students have a level of speed in understanding material. This is a concern for schools in improving the quality of education. The purpose of this study was to classify the level of activity of students at SMP ABC using the correlation between grades and the level of student activity who would be recommended to take part in competitions or prospective scholarship recipients. The data source that we used in this study came from the State Junior High School ABC which consists of several variables, including student attendance data, academic scores, psychomotor scores, and affective values. The method used in this research is the Clustering method with the K-means Algorithm. The results of this study can be grouped into 3 clusters including cluster 0 indicating active students as many as 30 students, cluster 1 showing inactive students as many as 8 students, and cluster 2 indicating less active students as many as 21 students.
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