Penerapan Algoritme Jaringan Syaraf Tiruan Backpropagation pada Pengklasifikasian Status Gizi Balita - Maria Sartika Tambun; Muhammad Tanzil Furqon; Agus Wahyu Widodo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Growth and development of children is an important thing that can be known by the assessment of nutritional status. A benchmark of a fulfilled accomplishment of nutrients in children who can be classified with severe obesity. Assessment of the nutritional status of toddlers can be determined by measuring the body known as "Anthropometry". In classifying the nutritional status of toddlers there is a concern that is on the community about the nutritional problems are good to know from many toddlers who are good nutrition, and also want to know which one is really the ideal nutrition. Because in the assessment of nutritional status of toddlers through good nutrition Antroprometry there is a large range. In the testing process using backpropagation method begins with the number of iterations, the value on the learning rate, the error limit and the amount of training and test data. In the study there are 3 input layer neurons, 3 hidden layer neurons and 1 output layer. The results of the test phase is obtained from the highest accuracy of 54.0% for the value of learning rate 0.1, the error limit of 0.001 and 0.005. The amount of train data and test data used is 80:10, with 10000 iteration. The lowest accuracy obtained is 43.0% ie on the results of the training data and test data is 70:50, and the learning rate of 0.3 and 1000 maximum iteration.