Optimasi Naïve Bayes Dan Algoritma Genetika Untuk Prediksi Penerimaan Beasiswa Pendidikan Pada SMP Utama
Abstract
Educational scholarships are one of the efforts to sustain students in getting a better education. Not a few students drop out in the middle of the road or cannot continue their education at the same level or higher level. Selection according to the criteria for scholarship recipients is important so that scholarships are on target. Similar to Depok Primary Middle School, educational scholarships are provided by schools based on 9 criteria for scholarship recipients, namely parent status, parent work, rented house, home appliances, vehicles, parents 'savings, parents' jewelry, cellphones and pocket money. With the number of prospective scholarship recipients there is an algorithm needed to accurately predict students who are entitled to scholarships. With the naïve bayes algorithm, accuracy is 77.50% in predicting scholarship recipients based on the criteria found in students. The use of genetic algorithms is done to get a more optimal level of accuracy. This is evidenced by the accuracy of 83.33%.
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E. Prasetyo. (2012). Data Mining : Konsep dan Aplikasi menggunakan MATLAB, 1st ed. Yogyakarta,Indonesia: Andi. E. Prasetyo. (2012). Data Mining : Konsep dan Aplikasi menggunakan MATLAB, 1st ed. Yogyakarta,Indonesia: Andi.
Kusrini dan Emha Tuafiq Luthfi. 2009. Algoritma Data Mining. Yogyakarta : Andi Offset.
Lahinta, Agus. 2009. Konsep Rancangan Sistem Pendukung Keputusan Penentuan Kandidat Penerima Beasiswa (Studi Kasus pada TPSDM Propinsi Gorontalo). Yogyakarta: Universitas Gadjah Mada
Murniasih, Erny. 2008. Winning A Scholarship. Jakarta : Gagas Media.
Murniasih, Erny. 2009. Buku Pintar Beasiswa. Jakarta: Gagas Media
Meilani, B. D., & Susanti, N. (2015). Aplikasi Data Mining Untuk Menghasilkan Pola Kelulusan Siswa Dengan Metode Naïve Bayes. Jurnal Ilmiah NERO, 1(3).
Obitko, M. (1998), web: http://www.obitko.com/tutorials/genetic-algorithms/index.php
Prasetyo, Eko. 2014. Data Mining – Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta : Andi Offset.
Santosa, Budi. 2007. Data Mining : Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta : Graha Ilmu.
Widodo, Prabowo Pudjo, Rahmadya Trias Handayanto, dan Herlawati. 2013. Penerapan Data Mining Dengan Matlab. Bandung : Penerbit Rekayasa Sains.
DOI: https://doi.org/10.31294/jtk.v5i2.5343
Copyright (c) 2019 Nining Suryani, Evy Priyanti
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
ISSN: 2442-2436 (print), and 2550-0120