Sistem Pendukung Keputusan Penentuan Tingkat Keparahan Autis Menggunakan Metode Fuzzy K-Nearest Neighbor - Robbiyatul Munawarah; Muhammad Tanzil Furqon; Lailil Muflikhah (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Autistic or Autistic Spectrum Disorders (ASD) is a general term referring to a neurodevelopmental disorder that is well known among Indonesian. Many researches on autism detection have been done by designing artificial intelligence systems with a variety of techniques used to make it easier for society to predict this kind of disorder. However, we hardly ever seen a system that can determine the severity of autism. In fact, the progress of the research in this field is no longer focused on whether a child is autistic individual or not, but rather to questioning about â€œIs there anything in autistic children that makes them different from one another?â€ as the â€˜severity' label appear to give them spesific class under certain behaviour they shown. To make it easier to determine the severity of autism, decision support system will be designed using one of data mining method called Fuzzy K-Nearest Neighbor (FK-NN). Fuzzy K-Nearest Neighbor (FK-NN) is K-Nearest Neighbor method combine with Fuzzy theory that gives value of membership on every predicted data.. There are 14 symptoms and 3 types of severity used as a parameter in the development of the system. The output of this decision support system is autism severity level. The results of the system shows that the average maximum accuracy is 90.83% while the average minimum accuracy is 82.50%. Based on those results, the uses of Fuzzy K-Nearest Neighbor (FK-NN) method can be implemented in our daily life.