PERBANDINGAN METODE k-NN DAN NEURAL NETWORK (Backpropagation) DALAM KLASIFIKASI GIZI ANAK - ; Arif Faizin (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan)
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),