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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Rekayasa dan Wirausaha
Mina Rizky, Muharratul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Penerapan Sistem Identifikasi Ekspresi Wajah Anak Penyandang Autisme Berbasiskan Citra Termal pada Sekolah Berkebutuhan Khusus di Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Mina Rizky, Muharratul; Leo, Hendrik; Fahmi, Fahmi; Dewi, Cut; Away, Yuwaldi; Misbahuddin, Misbahuddin
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v2i1.45635

Abstract

This community service activity aims to apply technology to detect facial expressions of children with autism through thermal images. The activity was carried out at My Hope Special Need Center, Banda Aceh, an educational center for orphans and children with special needs. By utilizing a combination of psychological and technological approaches, data collection is carried out in the form of thermal images of the faces of children with and without autism. The data obtained was analyzed using the Convolutional Neural Network (CNN) approach to develop an automatic facial expression detection method. The results of this activity show the potential use of facial recognition technology in supporting education and therapy for children with special needs.
Thermal Image Classification of Autistic Children Using Res-Net Architecture Ahmadiar, Ahmadiar; Melinda, Melinda; Muthiah, Zharifah; Zainal, Zulfan; Mina Rizky, Muharratul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/365fkd59

Abstract

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.
Co-Authors Ahmadiar, Ahmadiar Cut Dewi, Cut Fahmi Fahmi Leo, Hendrik Melinda Melinda Misbahuddin Misbahuddin Muhammad Irhamsyah Yunidar Yuwaldi Away Zainal, Zulfan Zharifah Muthiah