Open AccessOptimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis
Author(s) -
Linda Perdana Wanti,
Lina Puspitasari
Publication year - 2022
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v6i1.3599
Subject(s) - fuzzy logic , autism spectrum disorder , value (mathematics) , process (computing) , artificial intelligence , computer science , degree (music) , fuzzy inference system , precision and recall , autism , recall , mathematics , data mining , adaptive neuro fuzzy inference system , machine learning , fuzzy control system , psychology , cognitive psychology , developmental psychology , physics , acoustics , operating system
Diagnosis of autism spectrum disorder (ASD) can use a fuzzy inference system. The use of fuzzy logic method to obtain ASD diagnosis results according to experts based on the limits of factors/symptoms of the disease and all the rules obtained from experts. Recommendations for therapy and preventive actions can be given by experts after knowing the results of the diagnosis of ASD using the fuzzy logic method. This study serves to diagnose ASD by optimizing each degree of membership in the fuzzy logic method with the Mamdani method approach which is involved in the autism detection process involving 96 patient data. The Mamdani method itself can process an uncertain value from the user/patient into a definite value whose membership degree can be determined and adjusted to the conditions of the problem. Optimization was carried out on the degree of membership for all variables involved in the process of diagnosing ASD, namely social interaction, social communication and imagination and behavior patterns. The results of this study indicate a relatively small level of fuzzy calculation error with a precision value of 94.4%, a recall precision value of 65.4% and an error rate value of 3.05%. Calculation of accuracy shows a result of 90.59%.