Metode Data Mining untuk Seleksi Calon Mahasiswa Baru pada Penerimaan Mahasiswa Baru di Universitas Muhammadiyah Gresik

Mubarok, Muhammad Zaky Al (2024) Metode Data Mining untuk Seleksi Calon Mahasiswa Baru pada Penerimaan Mahasiswa Baru di Universitas Muhammadiyah Gresik. Jurnal Ilmiah Teknik Mesin, Elektro Dan Komputer, 4 (1). pp. 44-52. ISSN 2809-0799

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Abstract

Muhammadiyah University of Gresik is one of the educational institutions in Gresik. Every year, the ratio of new students to graduates is not the same, which can affect the accreditation of the campus. To address this issue, a prediction is made on the data of prospective new students to detect whether they can graduate on time or not. A comparison of the classification results is performed using the K-Nearest Neighbor and Naive Bayes methods. From the implementation and testing, Naive Bayes achieves an accuracy of 72%, while the K- Nearest Neighbor method achieves an accuracy of 64%. Therefore, Naive Bayes is better at classifying the data of prospective new students compared to K-Nearest Neighbor.

Item Type:	Article
Uncontrolled Keywords:	Classification, Thesis Completion Time, Weighted Naïve Bayes, K-Nearest Neighbor, Data Mining
Subjects:	Engineering > Informatics Engineering Engineering
Divisions:	Faculty of Engineering > Informatics Engineering Study Program
Depositing User:	Muhammad Zaky Al Mubarok
Date Deposited:	04 Aug 2025 02:03
Last Modified:	04 Aug 2025 02:03
URI:	http://eprints.umg.ac.id/id/eprint/14551

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