Data Mining Classification Untuk Prediksi Jumlah Mahasiswa Aktif dan Cuti Angkatan 2020 Menggunakan Metode K-Nearest Neighbor
DOI:
https://doi.org/10.30865/klik.v4i5.1795Keywords:
Aktif and Leave; Data Mining; Classification; K-Nearest NeighborAbstract
Active students are students who attend lectures, while students on leave are students who do not attend lectures. To remain active, students must re-register at the start of the semester. In this study, researchers used student data from the class of 2020 sourced from Bina Insan University, Lubuklinggau City. The method used is data mining. Data mining is a term used to describe the discovery of knowledge in databases. Data will be analyzed using the Python programming language with the K-Nearest Neighbor algorithm. In the classification of active and leave students, an accuracy value of 89% was obtained
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