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p-Index From 2021 - 2026
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This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Applied Engineering and Technological Science (JAETS)
Nurista Wahyu Kirana
Bandung State Polytechnic
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Backpropagation Artificial Neural Network For Classification Arrhythmia in ECG Signals  Nurista Wahyu Kirana; Ervin Masita Dewi; Vanesha Putri Anggita; Yana Sudarsa; Dodi Budiman Margana; Sugondo Hadiyoso
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8413

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, accounting for approximately 17.9 million deaths annually. Among these, arrhythmias represent a significant concern due to their potential to lead to severe cardiac events. Traditional methods for detecting arrhythmias often require specialized equipment and healthcare facilities, which may not be readily accessible, especially in remote areas. This paper proposes the development of a portable electrocardiogram (ECG) device integrated with an Artificial Neural Network (ANN) using the Backpropagation algorithm to classify arrhythmias, thereby facilitating early detection and management. Arrhythmia is a heart condition characterized by an irregular heartbeat, where the heart may beat faster or slower than normal. Classification of arrhythmia can assist patients in monitoring their heart condition without needing to visit the hospital. This final project implements the Artificial Neural Network (ANN) method due to its ability to perform fast and accurate classifications. Prior to classification, feature extraction is carried out to detect the R wave interval, T wave interval, and the differences between the R and T wave intervals. The classification results are then displayed through a graphical user interface (GUI). The development of this ANN-based arrhythmia signal classification tool aims to help patients detect heart abnormalities at an early stage, potentially preventing the condition from worsening. Testing was conducted on 11 individuals, with 9 identified as having normal heart signals and 2 diagnosed with arrhythmia. When compared to a simulator, the classification system achieved 100% accuracy.
Analyzing the impact of sports activity intensity on muscle capacity through integrated biosensor technology Ervin Masita Dewi; Nurista Wahyu Kirana; Sugondo Hadiyoso
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26264

Abstract

In the past few years, biosensor technology has paved the way for new insights into the physiological effects of physical exercise. Quantitative analysis, especially in the case of muscle capacity measurement, is the focus of studies to assess the impact of sports activities. Therefore, this study examines the impact of sports activity intensity on muscle capacity using an integrated biosensor system developed at Bandung State Polytechnic. Surface electromyography (sEMG) measurements were conducted on 30 participants aged 20–25 during various sports activities. Results showed a strong positive correlation (r=0.814) between sports activity frequency and muscle contraction, suggesting higher activity correlates with increased muscle activity. Conversely, the correlation during muscle relaxation was low (r=0.261), indicating independence from sports activity. In the future, it is expected that integrated biosensors will have the ability to concurrently measure and monitor various parameters like heart rate (via electrocardiogram), blood oxygen levels (via photoplethysmography), and blood pressure. The integrated biosensor system allows for comprehensive assessment and optimization of sports performance and injury prevention strategies.
Co-Authors Dodi Budiman Margana Ervin Masita Dewi Sugondo Hadiyoso Vanesha Putri Anggita Yana Sudarsa