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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Padjadjaran Journal of Dentistry JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Rekayasa elektrika Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Oktiana, Maulisa
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Public Health

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Comparative Analysis of Homomorphic and Morphological Filters Using Inception V3 for Thermal Facial Image Classification of Autistic Children Catur Andryani, Nur Afny; Melinda, Melinda; Tariliani, Cut Dara; Oktiana, Maulisa; Junidar, Junidar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2885

Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by varying degrees of difficulty in social interaction and communication and repetitive behaviors. Early confirmation of the diagnosis of ASD leads to early appropriate treatment. However, confirming ASD diagnosis is challenging due to its wide spectrum and challenging behavior assessment. This research proposes a technology-based ASD diagnosis on children utilizing thermal facial analysis. This is conducted subject to the uniqueness of facial expression that is typically applied to children with ASD. A modified Inception V3 architecture did the intended thermal facial analysis for ASD diagnosis. Homomorphic filters and morphological filters are applied to the data pre-processing to improve the classification ability. The proposed identification method shows better sensitivity to the false-positive aspect. It is indicated by better performance in terms of precision, with a rate of 90% to 91%. This research is expected to support medical experts in confirming early diagnosis in children with ASD.
Image Segmentation Performance using Deeplabv3+ with Resnet-50 on Autism Facial Classification Melinda, Melinda; Aqif, Hurriyatul; Junidar, Junidar; Oktiana, Maulisa; Binti Basir, Nurlida; Afdhal, Afdhal; Zainal, Zulfan
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1144

Abstract

In recent years, significant advancements in facial recognition technology have been marked by the prominent use of convolutional neural networks (CNN), particularly in identification applications. This study introduces a novel approach to face recognition by employing ResNet-50 in conjunction with the DeepLabV3 segmentation method. The primary focus of this research lies in the thorough analysis of ResNet-50's performance both without and with the integration of DeepLabV3+ segmentation, specifically in the context of datasets comprising faces of children on the autism spectrum (ASD). The utilization of DeepLabV3+ serves a dual purpose: firstly, to mitigate noise within the datasets, and secondly, to eliminate unnecessary features, ultimately enhancing overall accuracy. Initial results obtained from datasets without segmentation demonstrate a commendable accuracy of 83.7%. However, the integration of DeepLabV3+ yields a substantial improvement, with accuracy soaring to 85.9%. The success of DeepLabV3+ in effectively segmenting and reducing noise within the dataset underscores its pivotal role in refining facial recognition accuracy. In essence, this study underscores the pivotal role of DeepLabV3+ in the realm of facial recognition, showcasing its efficacy in reducing noise and eliminating extraneous features from datasets. The tangible outcome of increased accuracy of 85.9% post-segmentation lends credence to the assertion that DeepLabV3+ significantly contributes to refining the precision of facial recognition systems, particularly when dealing with datasets featuring faces of children on the autism spectrum.
Co-Authors . Roslidar Afdhal Afdhal Afnan, Afnan Al Bahri Aqif, Hurriyatul Bintang, Akhyar Binti Basir, Nurlida Catur Andryani, Nur Afny Chairullah, Chairullah Elma, Yasmina Ernita Dewi Meutia Fitri Arnia Habibie, Hafidh Hargiani, Fransisca Xaveria Isma, Teuku Reza Auliandra Janura, Subhan Junidar, Junidar Lailatul Qadri Zakaria Maya Fitria Melinda Melinda Muchtar, Kahlil Muhajir Muhajir, Muhajir Mulyadi Mulyadi Muzammil, Rivaul Novandri, Andri Noviantika, Fina Novita, Rizki Putri, Rizkika Rafiki, Aufa Rahayu, Handika Rahmatika, Nisa Adilla Ramiady, Luthfiar Saddami, Khairun Sayed Muchallil Tariliani, Cut Dara Yassar, Muhammad Keysha Al Yudha Nurdin Yufnanda, Muhammad Aditya Yuwaldi Away Zainal, Zulfan Zulfikar