Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak - Indri Monika Parapat; Muhammad Tanzil Furqon; Sutrisno Sutrisno (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Growth and development of children at an early age affect the child's personal ability in the future. Every child is unique, so growth and growth are different. Deviation of late child growth is known to result in long-term and difficult to repair. . Based on these problems, this research was conducted by using the sUPPmethod for the classification of child growth deviations. ELM method consists of training process as system learning and testing to obtain the result of classification. The parameters test are test of lambda, complexity, and maximal iteration. There are 90 data used in this research, which is divided into 3 classes. Classes in this study represent three types of diseases in growth and development are Down Syndrome, Autisme, dan Attention Deficit Hyperactivity Disorder (ADHD). Basically SVM algorithm is a method of linier classification, so there is kernel is used to overcome nonlinier data. The final result of this study produced the highest average accuracy on this research is 73,78% Î» = 0,1, C = 0,1, itermax = 10 and also using polynomial kernel. The comparison of the result of the classification of child growth deviation with the help of psychologist shows that the system produces poor accuracy. This can be due to the small and unbalanced data used for the research.