Abstract
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%.
Highlights
Diagnosis of autism spectrum disorder (ASD) can use a fuzzy inference system
Introduction which is called an expert system [6], The healthy development of children and in accordance with the pattern of growth and development as children get older is the hope of every parent for their beloved baby
By inputting all the symptoms that occur in children into the knowledge base of the system, the expert system can be used to diagnose developmental disorders that occur without time and place restrictions [8], [9]
Summary
The fourth stage is calculating the predicate rule (α) which is used in the knowledge base of the expert system. The calculation of the value of the rule predicate is usually used for solving a complex problem by adding obtained from the process of implication of each rule. To deal with complex problems obtained by entering the minimum value of the degree of and difficult to define with a mathematical model with membership between the variables that have been an approach reasoning process, this fuzzy system is good combined in a predetermined rule [38]. The lower threshold value is at the point and the upper threshold value is at the point
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