Identifikasi Penyimpangan Tumbuh Kembang Anak Dengan Algoritme Backpropagation - Fadhilla Puji Cahyani; Muhammad Tanzil Furqon; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Growth and development are two processes that are interdependent and inseparable. Growth and development of children greatly affect the quality of growth and development of children in the future. In the development phase, often encountered irregularities that cause delay in child development when compared to children of the same age. Developmental disorders that often occurred in children are such as autism, Attention Deficit Disorder (ADHD), and Down Syndrome. This study aims to identify the type of development disorder of children based on symptoms that appear using Backpropagation algorithm. Backpropagation algorithm is one of Artificial Neural Network algorithm that has ability to solve complex problems that can not be solved by conventional learning technique. The network architectures used in this study are 38 input neurons, 5 hidden neurons, and 3 output neurons. The results of this study indicate that Backpropagation algorithm can identify the development disorder of children well with the average accuracy of 91,11% in the test of training data of 81, 9 testing data, learning rate 0,1, and 0,0009 minimun error.