Pengembangan Model Deteksi Autism Spectrum Disorder (ASD) Dengan Algoritma Facenet Vggface Dan Insightface

Marsha Falen Fransisca (Universitas Bina Insan Lubuklinggau)
Lukman Sunardi (Universitas Bina Insan Lubuklinggau)
Harma Oktafia LW (Universitas Bina Insan Lubuklinggau)
Budi Santoso (Universitas Bina Insan Lubuklinggau)

Bulletin of Information Technology (BIT)
Vol 7 No 2 (2026)

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interaction, and behavior. Conventional ASD diagnosis relies on clinical observation, which is time-consuming and subjective. Therefore, an automated approach using artificial intelligence is required to support early detection. This study proposes an ASD detection model based on facial image analysis using deep learning approaches, namely FaceNet, VGGFace2, and InsightFace as facial feature extraction methods. The dataset consists of 3,620 facial images categorized into ASD and non-ASD classes. The research process includes preprocessing, feature extraction, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all models achieved good classification performance, with FaceNet achieving the highest accuracy of 98%, followed by InsightFace with 96%, and VGGFace2 with 95%. These findings demonstrate that face embedding-based models provide superior feature extraction capabilities for ASD detection.