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PERBANDINGAN METODE k-NN DAN NEURAL NETWORK (Backpropagation) DALAM KLASIFIKASI GIZI ANAK

Explore IT! : Jurnal Keilmuan dan Aplikasi Teknik Informatika

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Title PERBANDINGAN METODE k-NN DAN NEURAL NETWORK (Backpropagation) DALAM KLASIFIKASI GIZI ANAK
 
Creator —
Arif Faizin
 
Description In the Decree of the Minister of Health of the Republic of Indonesia No. 1995 / Menkes / SK / XII / 2010 dated December 30, 2010 and the World Health Organization- National Center for Health Statistics (NCHS-WHO), it is clear how standardization child nutrition is a very urgent matter. Because to know the nutritional intake of children that must be met when the condition of the child in a state of malnutrition or if the child nutrition. In this case, the child nutrition will affect brain development; adequate nutrition will be able to add to the absorption of the brain which will give the maximum intelligence, which corresponds to the opening 45 that is to educate intelligence UDD Nations children, which will be the focus point of this research. Methods to be used are two methods of data mining classification that Neural Network and K-Nearest Neighbor (K-NN), which will be sought method best of both methods, in seeking highest accuracy.
Keynotes: Child Nutrition, Neural Networks and K-Nearest Neighbor (K-NN),
 
Publisher Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan
 
Date 2019-11-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://jurnal.yudharta.ac.id/v2/index.php/EXPLORE-IT/article/view/1716
10.35891/explorit.v10i1.1716
 
Source 2549-354X
2086-3489
10.35891/explorit.v10i1
 
Language eng
 
Relation https://jurnal.yudharta.ac.id/v2/index.php/EXPLORE-IT/article/view/1716/1337
 
Rights Copyright (c) 2018 Explore IT! : Jurnal Keilmuan dan Aplikasi Teknik Informatika
https://creativecommons.org/licenses/by/4.0