Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder
Diagnosis

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder
Diagnosis

: Autism Spectrum Disorder, Expert System, Optimization, Fuzzy Logic Method

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%.

Linda Perdana Wanti1
, Lina Puspitasari2

, Politeknik Negeri Cilacap

1 februari 2022

Fajar bagus W

PDF

Indonesia

Text