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All Journal Kitektro InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Green Intelligent Systems and Applications Jurnal INFOTEL Jurnal Polimesin
Yunidar Yunidar
Department Of Elect Rical Engineering And Computer, Engineering Faculty, University Of Syiah Kuala, Banda Aceh, Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Electrocardiogram Signal Analysis Based on Discrete Wavelet Transform with Machine Learning Method in Autistic Children 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 (Article in Progress)
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 manage emotions, interact socially, and respond to the environment. The main challenge in monitoring children's physiological condition is the limited availability of objective observation methods that rely heavily on health professionals. One potential objective approach is to analyze the ECG signal. However, ECG signals in children with ASD generally have high levels of noise due to body movements during recording, making manual analysis and conventional methods difficult. This study aims to develop a classification system for the physiological condition of children with ASD based on ECG signals, specifically to distinguish between quiet and active states. The dataset consists of 1000 from each of the two active classes and 1000 from the quiet class. ECG signals were processed using DWT for filtering, and then classified using three machine learning algorithms: SVM, RF, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that Random Forest provided the best performance, with an accuracy value of 93%. Meanwhile, SVM achieved an accuracy of 91.25%, while AdaBoost showed slightly lower performance at 90.00%. Based on these results, Random Forest was selected as the most optimal model and integrated into a web-based system using Streamlit. This study demonstrates that the combination of DWT and Random Forest is effective for classifying the physiological conditions of autistic children and has the potential to serve as an objective tool for monitoring them.
Co-Authors Achmi Yuliani Al Bahri Alfatirta Mufti Elizar Elizar Hanum Aulia I Ketut Agung Enriko Iskandar Hasanuddin Khairani Khairani M Fahrur Rozi Melinda Melinda Melinda Melinda Mohd. Syaryadhi Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhsin Muhsin Nur Afny Catur Andryani Nurlida Basir Nurlida Basir Nurrizqa Nurrizqa Riska Sufina Rizal Munadi Shaquille Rizki Ramadhan Na Syahrial Syahrial Syahrial Syarifah Rauzatul Jannah Yudha Nurdin Yuwaldi Away