PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP

Abstract

The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.