Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto menggunakan Algoritma Genetika untuk Diagnosis Autisme pada Anak - Indra Eka Mandriana; Candra Dewi; Muhammad Tanzil Furqon (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Autism is a developmental disorder that cause children to experience social disruption in certain areas, such as communications, social interaction, emotional and behavioral symptoms that is difficult to be identified. According to research in autism, the number of children who suffered from autism is estimated to grow every year around the world, including in Indonesia. This research implement Fuzzy Tsukamoto method to optimized genetic algorithm in order to diagnose autism in children, by optimizing the constraints on all fuzzy variables.Chromosome representation that is used in this research is real code genetic algorithm which every chromosome will initialize the limitations on all fuzzy variables. Method that is used to the process of crossover is extended intermediate crossover and random mutation for mutation process while selection method used elitism selection. Based on the results, the system obtained the most optimal parameters on a method of CARS in a population of 50, 200 generations, as well as the combination of Cr = 0.8 and Mr = 0.1 with the fitness of 1, while on the CHAT population method 10, 100 generations, as well as the combination of Cr = 0.9 and Mr = 0.1 with fitness by 1