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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Ranggagading (JIR) Sinergi Fakultas Ekonomi JURNAL ILMIAH SOCIETY Kitektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Sari Pediatri Jurnal Kedokteran Gigi Universitas Padjadjaran Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Economic, Bussines and Accounting (COSTING) JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Kebijakan Perikanan Indonesia Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT JETL (Journal Of Education, Teaching and Learning) INTERNATIONAL JOURNAL OF NURSING AND MIDWIFERY SCIENCE (IJNMS) JISIP: Jurnal Ilmu Sosial dan Pendidikan Journal of Electronics, Electromedical Engineering, and Medical Informatics JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Pemerintahan dan Politik Jurnal Ilmiah Edunomika (JIE) International Journal of Economics Development Research (IJEDR) Indonesian Journal of Electrical Engineering and Computer Science Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Cahaya Mandalika Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Journal of Government Science (GovSci ) : Jurnal Ilmu Pemerintahan Journal of Emerging Business Management and Entrepreneurship Studies Green Intelligent Systems and Applications JTechLP Jurnal Pengabdian Masyarakat Bangsa Jurnal Rekayasa elektrika Journal of Computing Theories and Applications Triwikrama: Jurnal Ilmu Sosial JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal INFOTEL Akuntansi: Jurnal Riset Ilmu Akuntansi GEMBIRA (Pengabdian Kepada Masyarakat) Indonesian Journal of Community Services Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Sriwijaya Electrical and Computer Engineering (Selco) Journal Jurnal Pengabdian Rekayasa dan Wirausaha Jurnal Teknokes Nawadeepa: Jurnal Pengabdian Masyarakat Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Pengabdian Rekayasa dan Wirausaha
. Melinda
Department Of Electrical And Computer Engineering, Faculty Of Engineering, Universitas Syiah Kuala
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Journal : joiv international journal on informatics visualization

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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.
Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda, Melinda; Maulisa, Oktiana; Nabila, Nissa Hasna; Yunidar, Yunidar; Enriko, I Ketut Agung
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.
Co-Authors . Roslidar Aafiyah, Siti Afra Abdurohim Abdurohim, Abdurohim Abed Nego, Abed Abrina Anggraini, Sinar Perbawani Achmad Maqsudi, Achmad Achmad, Ilham Afnan, Afnan Agnesia Candra Sulyani Agung Hilm Ghimri Ahmad, R. Andriadi Ahmadiar, Ahmadiar Ainil Syafitri Akbar, Alif Yafi Al Bahri Alam Mahadika, Alam Mahadika Albar, Nizam Alfatirta Mufti Alfatirta Mufti Alfian, Ridho Alfita Khairah Alfita Khairah Alifia, Rania Sofie Amalia Amalia Amaliatulwalidain, Amaliatulwalidain Ameilia Zuliyanti Siregar Ameilia Zuliyanti Siregar Anabel, Cendana Ananda, Mulya Anik Puryatni Anto Ariyanto Anzelina, Dhea Eprillia Aqif, Hurriyatul Ari Rahmat Putra Ibina Ariyani, Amra Arsy Febrina Dewi Arumi, Naila Azaria Arya Suyanda Asriati Asriati, Asriati Astuti, Meti Aufa Rafiki Aufa Rafiki Aufa Rafiki Aufa Rafiki Aufa Rafiki Aulia Arafat Aulia Rahman Aurelia, Gabrella Azhari, Rizki AZMI, MUHAMMAD RAUDHI Azra, Ery Bashir, Nurlida Basir, Nurlida Basuki Toto Rahmanto Bil Haki, Arif Binti Basir, Nurlida Budi Arianto Buleun Waladah Catur Andryani, Nur Afny Cloudya, Cindy Cut Salsabilla Azra Cut Siti Azola Syiva D Acula, Donata Diana Novita Diana, Fitri Dini, Siti Doke, Herlina Theodensia D. 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