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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Inotera Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Brilliance: Research of Artificial Intelligence International Journal Software Engineering and Computer Science (IJSECS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics PERFECT: Journal of Smart Algorithms Sriwijaya Electrical and Computer Engineering (Selco) Journal Jurnal Pengabdian Rekayasa dan Wirausaha
Muharratul Mina Rizky
Universitas Syiah Kuala
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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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.
Classification of autism features in electroencephalography recordings using random forest method Melinda, Melinda; Zahran Jemi , Faris; Muliyadi, Muliyadi; Safitri, Rini; Mina Rizky , Muharratul; Duana, Maiza
Sriwijaya Electrical and Computer Engineering Journal Vol. 1 No. 2 (2024): Sriwijaya Electrical and Computer Engineering Journal
Publisher : Control and Computational Intelligent System (CoCIS) Research Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62420/selco.v1i2.9

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that significantly impacts communication, social interaction, and behavior in children, often leading to withdrawal, repetitive behaviors, and difficulties with eye contact. Traditional diagnostic methods primarily relied on behavioral assessments, which have proven insufficient in accuracy. This study aims to enhance ASD diagnosis by employing Electroencephalography (EEG) as an objective marker to differentiate between individuals with ASD and neurotypical individuals. Utilizing a dataset from King Abdulaziz University comprising 16 children—4 neurotypical and 12 with ASD—this research implements preprocessing techniques such as Independent Component Analysis (ICA) to eliminate noise and artifacts from EEG signals. Following this, Wavelet Packet Decomposition (WPD) is applied at three levels to improve signal resolution. Statistical features including mean, variance, skewness, and kurtosis are extracted for classification purposes. The Random Forest (RF) method is then employed for classification, achieving an accuracy of 76.8%. However, classification errors predominantly arise from the imbalance in the dataset, with more data available for ASD subjects compared to neurotypical subjects. The findings reveal significant differences in statistical features between the two groups, indicating the potential of EEG technology and computational algorithms in developing a more accurate and objective ASD diagnosis system. This research contributes valuable insights for early intervention strategies and future studies aimed at improving diagnostic methodologies for children with autism.
Penerapan Sistem Pengelolaan Air Sisa Wudhu Otomatis Berbasis IoT di Mushalla Darul Faizin, Desa Kopelma Darussalam Amanda, Silviani; Yufnanda, Muhammad Aditya; Rizky, Muharratul Mina; Ramadhana, Rizka; Aulia, Niza; Dawood, Rahmad; Leo, Hendrik
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Mushalla Darul Faizin, Banda Aceh, yang menghadapi permasalahan tingginya konsumsi air bersih akibat belum adanya sistem pengelolaan air wudhu yang efisien. Air sisa wudhu yang masih tergolong layak digunakan kembali selama ini langsung dibuang ke saluran pembuangan tanpa pemanfaatan lebih lanjut. Melalui kegiatan ini, tim pengabdian mengembangkan dan menerapkan sistem daur ulang air wudhu otomatis berbasis Internet of Things (IoT) yang berfungsi untuk mendeteksi, mengumpulkan, menyaring, serta mendistribusikan kembali air sisa wudhu untuk keperluan non-konsumsi seperti pembersihan lantai dan penyiraman tanaman. Sistem ini menggunakan mikrokontroler ESP32, sensor ultrasonik untuk pemantauan level air secara real-time, serta integrasi platform Telegram bot untuk memudahkan pengurus mushalla dalam melakukan monitoring dan kontrol jarak jauh. Hasil implementasi menunjukkan bahwa sistem beroperasi stabil dengan reliabilitas mencapai 98,2% dan mampu menghemat penggunaan air bersih hingga sekitar 30%. Selain menghasilkan solusi teknologis yang efisien, kegiatan ini juga meningkatkan kesadaran dan kapasitas pengurus mushalla dalam pengelolaan sumber daya air secara mandiri dan berkelanjutan.
Enhancing Face Detection Performance in Low-Light Conditions Using NIR Thermal Imaging and Image Morphology Maulisa Oktiana; Cut Salsabilla Azra; Rusdha Muharar; Fajrul Islamy; Rizka Ramadhana; Melinda Melinda; Niza Aulia; Muharratul Mina Rizky; Maya Fitria
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 2 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.2.108786

Abstract

Face detection plays a vital role in biometric, security, and surveillance systems. Conventional approaches based on the visible light (VIS) spectrum often suffer performance degradation under poor lighting conditions, limiting their reliability. To address this issue, this study employs thermal imagery in the Near-Infrared (NIR) spectrum, which is less affected by ambient light, combined with image morphology operations to enhance segmentation accuracy. Experiments were conducted using the LDHF-DB dataset (300 images at distances of 1 m, 60 m, and 100 m) and a subset of the Tuft dataset (60 images). Face detection was performed using the HOG + SVM method, followed by Otsu thresholding and morphological operations. Performance was evaluated using Peak Signal-to-Noise Ratio (PSNR). Results show that applying morphological operations significantly improves PSNR values, with an average increase of more than 35%. The best performance was achieved on the 1 m subset, while longer distances presented greater challenges. These findings highlight the potential of integrating NIR thermal imagery and morphological processing to improve the robustness and reliability of face detection systems in low-light environments.
Online Digital Invitation (An Implementation with Go-Web) Adi Ahmad; M. Arinal Ihsan; Hanis Novansyah; Muharratul Mina Rizky; Bakruddin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 2 No. 2 (2022): OCTOBER 2022
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v2i2.802

Abstract

This study aims to develop an online digital invitation service that can be used for all activities such as invitations to religious, family, and personal activities and in the application development process the Go Web framework is used. This study uses research with the Research and Development method. The product developed based on initial research is the Online Digital Invitation System. The test subjects in this development are expert subjects and students of STMIK Indonesia Banda Aceh as potential users of the product. This research was taken by random sampling technique, which consisted of 20 small-scale and 30 large-scale test people. The data collection technique was done by using a questionnaire. This questionnaire was conducted to assess the application developed from the completeness of the application and the material as well as the physical appearance of the application. Data analysis is descriptive quantitative and qualitative. Based on the results of research and discussion of research results to develop an Online Digital Invitation System, several stages of feasibility testing are needed, namely media expert tests, material expert tests and tests on respondents. Based on the results of research on small group trials, the Digital Online Invitation System was obtained. Most 90% stated that it was very feasible to use, and the results of research on large trials, most of the students, 96.67% stated that it was very feasible to use. With these results, it can be concluded that the Online Digital Invitation System is very feasible to use. Based on the conclusions from the results of the study, it is implied that the Online Digital Invitation System is very feasible to use, so that it becomes good input for users in making digital invitations both used through browsers and android.
Co-Authors Adi Ahmad Afdal Afdal Ahmadiar, Ahmadiar Amanda, Silviani Anhar Pulungan ANNISA KARIMA Asmaidi Asmaidi Aulia, Niza Bakruddin Cut Salsabilla Azra Darma Setiawan Putra Duana, Maiza Fahmi Fahmi Fajrul Islamy Fardiansyah Adami Ginting, Depi Hanis Novansyah Ilham, Dirja Nur Leo, Hendrik Lindawati Lindawati M. Arinal Ihsan Maulisa Oktiana Maya Fitria Melinda Melinda Melinda Melinda Mislina Mislina Muhammad Irhamsyah Muhammad Khoiruddin Harahap Muliyadi Muliyadi Niza Aulia Permata, Riski Surya R, Fitria Ulfa Rahmad Dawood Ramadhana, Rizka Ridha Sitorus Rini Safitri Rizka Ramadhana Rusdha Muharar Sabrina Ulia Mouna Safridatul Audah Sepri Kurniadi Sukiman, T. Sukma Achriadi Ulya, Athiyatul Yufnanda, Muhammad Aditya Yunidar Zahran Jemi , Faris Zainal, Zulfan Zharifah Muthiah Zulfia , Anni