Clustering Data Menggunakan Metode K-Means untuk Rekomendasikan Pembelajaran Akademik bagi Siswa Aktif dalam Ekstrakurikuler
DOI:
https://doi.org/10.30865/klik.v4i1.1116Keywords:
Learning Methods; Academic Performance; Extracurricular; K-Means Clustering; Data Mining; Major RecommendationsAbstract
Student academic achievement includes their achievements and performance in academic fields, such as exam results, report card scores, and achievements in certain subjects. The student's academic achievement has an effect on further study to a higher level. In this study, grouping of student data was carried out using the K-Means Clustering algorithm. The data used are grades VIII and IX grade report cards. The results of grouping the data showed that there were eight clusters with different numbers of students using three categories of cluster sizes, namely smart, moderate, and moderate. Based on data analysis, it was found that English needs to get special attention in learning. Students with balanced grades between science and social studies can choose a major according to their interests. Students in the moderate cluster tend to have dominant grades in the Science major, while students in the moderate cluster tend to have dominant grades in the Social Sciences major. The majority of students have grades above 80, indicating good performance. The grouping of these data forms the basis for selecting appropriate majors, optimal participation in extracurricular activities, and support for students with sufficient grades. It is hoped that the research results will become a recommendation for schools to improve students' understanding and maintain a balance between academic and extracurricular activities
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