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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Intelektualita: Keislaman, Sosial, dan Sains POSITIF Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Ilmiah Matrik INOVTEK Polbeng - Seri Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Global JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Mantik Jurnal Muara Ilmu Ekonomi dan Bisnis Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) International Journal of Advanced Science Computing and Engineering Bulletin of Social Informatics Theory and Application
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Journal : Jurnal Intelektualita: Keislaman, Sosial, dan Sains

A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students Nurhachita Nurhachita; Edi Surya Negara
Intelektualita Vol 9 No 1 (2020): Jurnal Intelektualita: Keislaman, Sosial, dan Sains
Publisher : Wakil Rektor III Bidang Kemahasiswaan dan Kerjasama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/intelektualita.v9i1.5574

Abstract

The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. so that there is an accumulation of student data continuously. The purpose of this study is to compare the K-Means Clustering Algorithm and Naïve Bayes on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is Knowledge Discovery in Database (KDD). The tools used are Rapid Miner. The attributes used are national examination score, school origin, and study programs. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used the K-Means Clustering Algorithm to produce 3 clusters, while the Naïve Bayes results resulted in an accuracy value of 9.08%.