Autism Spectrum Disorder (ASD) is a developmental disability caused by differences in the brain. Autism disorders usually appear in children before the age of three to seven years. However, ASD in children is only detected after the age of ten years old. In this study, a detection system based on fuzzy logic decision support was designed to detect the severity of ASD in children. The detection process was carried out by asking parents questions about the child's condition as a detection reference taken from the ASD detection module. This system was also equipped with instrumentation in the form of wearable sensors in the form of accelerometers and gyroscopes that function to read flapping hand movement signals in children as an indication of repetitive behaviors suffered by ASD children. The results showed that the assessment form used, namely M-CHART - R, proved to be quite accurate in detecting the initial condition of the subject. The flapping hand detection sensor detected flapping hands that appeared on the subject with an average error of 0.356133 for sensor module 1 and 0.30866 for sensor module 2. The system has 83.3% accuracy. Future development can be addressed to hardware and software development as an embedded system so that the entire system can work in real-time using IoT.