Deteksi Dini Autisme Anak Menggunakan Fuzzy Analytical Hierarchy Process (FAHP)

Abstract

Children with Autism Spectrum Disorder (ASD) often face significant challenges in conventional educational settings. This is exacerbated by the lack of accurate early detection and often leads to delayed diagnosis. This results in academic difficulties for children with autism spectrum disorder. The research team proposed a Fuzzy Logic-based conceptual model, specifically applying the Fuzzy Analytical Hierarchy Process (FAHP), as a tool for early detection of autism potential in children.
The FAHP approach was chosen due to its ability to handle uncertainty and ambiguity that are closely related to the assessment of child characteristics, thus enabling more objective identification. The data used in this research is sourced from the Kaggle dataset which consists of 6075 records, 14 attributes, and 1 label. The dataset obtained is cleaned and then processed using FAHP. The results of this study are FAHP scores and autism risk levels. The findings indicate that a fuzzy logic-based approach can be used for early detection of autism, although wider empirical validation is needed from experts such as psychologists.