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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control International Journal of Engineering, Science and Information Technology Jurnal Pengabdian Rekayasa dan Wirausaha
Muhammad Irhamsyah
Universitas Syiah Kuala
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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ECG Signal-based Classification of Physiological States in ASD Children DWT and Machine Learning Muhammad Irhamsyah; Hanum Aulia; Yunidar Yunidar; Melinda Melinda; Muhsin Muhsin; Syarifah Rauzatul Jannah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2750

Abstract

ASD is a neurodevelopmental disorder that affects a child's ability to regulate emotions, interact socially, and respond to environmental stimuli. Monitoring physiological conditions in children with ASD is challenging because it relies on subjective observations. In this study, physiological conditions are defined based on observable behavioral states, namely active and quiet. The active state reflects increased physiological arousal and a higher heart rate, while the quiet state represents a resting state with a more stable heart rate. This study proposes an ECG-based classification system to distinguish between these two states. The dataset consists of 2000 samples for each class. Due to noise in the ECG signal caused by body movements, preprocessing was performed using the DWT to improve signal quality. The processed signals were then classified using three machine learning algorithms: SVM, Random Forest, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score. The results showed that without DWT, Random Forest achieved the highest accuracy of 91.00%, followed by SVM at 88.87%, and AdaBoost at 87.25%. While using DWT, Random Forest achieved the highest accuracy of 93.75%, followed by SVM at 91.37%, and AdaBoost at 90.25%. This indicates that DWT can produce better signal quality. Furthermore, Random Forest was selected as the optimal model and implemented in a Streamlit-based web application for real-time monitoring. These findings indicate that the combination of DWT and Random Forest is effective for classifying physiological conditions in children with ASD and has potential as an objective monitoring tool.
Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar Yunidar; Melinda Melinda; Mauliza Putri; Muhammad Irhamsyah; Nurlida Basir; Alfita Khairah
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1253

Abstract

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.
Penerapan Sistem Identifikasi Ekspresi Wajah Anak Penyandang Autisme Berbasiskan Citra Termal pada Sekolah Berkebutuhan Khusus di Banda Aceh Melinda Melinda; Yunidar Yunidar; Muhammad Irhamsyah; Muharratul Mina Rizky; Hendrik Leo; Fahmi Fahmi
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

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

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.
Co-Authors . Melinda Alfita Khairah Fahmi Fahmi Hanum Aulia Hendrik Leo Mauliza Putri Muharratul Mina Rizky Muhsin Muhsin Nurlida Basir Syarifah Rauzatul Jannah Yunidar Yunidar