Penggunaan Metode K-Means Untuk Menentukan Clustering Kelompok Belajar Siswa
Keywords:
Students, UTS Score, Clustering, K-Means
Abstract
Science and technology will facilitate human work. However, on the other hand it will increase competition. In facing intense competition, it is necessary to have competent human resources. Students are expected to be academically prepared, in the form of knowledge and skill readiness to face increasingly fierce competition. One way to see student competence is to look at learning outcomes that can be represented by the exam scores taken. The midterm exam (UTS) is a form of exam which is an assessment component. By knowing the UTS scores, the lecturer knows the distribution of students in terms of academic competence. For this reason, it is necessary to group (clustering) using the k-means algorithm as a consideration for lecturers in forming student study groups based on UTS value clusters.References
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[9] M. R. Firdaus, A. Latif, and W. Gata, “Klasifikasi Kelayakan Calon Pendonor Darah Menggunakan Neura L Network,” Sistemasi, vol. 9, no. 2, p. 362, 2020, doi: 10.32520/stmsi.v9i2.840.
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Published
2022-12-04
How to Cite
[1]
Muhammad Rifqi Firdaus and Amin Nur Rais, “Penggunaan Metode K-Means Untuk Menentukan Clustering Kelompok Belajar Siswa”, ELKOM, vol. 15, no. 2, pp. 434-442, Dec. 2022.
Section
Articles
