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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Dzakiroh Mufidah Hasibuan
Universitas Syiah Kuala

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Enhanced detection of chronic obstructive pulmonary disease via exhaled breath analysis: internet of things and electronic nose system Nur Hidayah Naimah Harahap; Budi Yanti; Muhammad Ilham; Muhammad Suhaili; Dzakiroh Mufidah Hasibuan; Farah Narizki
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp875-883

Abstract

Chronic obstructive pulmonary disease (COPD) remains a major global health burden, highlighting the need for accessible, non-invasive screening tools. This study aims to develop a portable, real-time internet of things (IoT)-integrated electronic nose (e-nose) system for COPD detection using exhaled volatile organic compounds (VOCs). Breath samples from 44 participants (healthy, smokers, and COPD) were analyzed using a MOS based e-nose, and four machine-learning classifiers were evaluated. Data were processed through cloud-based pipelines enabling real-time acquisition and automated analysis. The random forest (RF) model achieved the highest performance (accuracy 86%) in distinguishing COPD-related VOC patterns. This approach overcomes limitations of earlier offline Tedlar-bag methods by enabling direct, real-time breath analysis. The prototype dashboard provides immediate visualization for potential remote monitoring. Key limitations include the small sample size and non-standardized breath sampling, which may affect VOC variability. Overall, this work contributes a cost-effective, portable, IoT-enabled framework demonstrating the feasibility of real-time VOC analysis for early COPD screening and future integration into telehealth and community-based diagnostics.
Co-Authors Budi Yanti Farah Narizki Muhammad Ilham Muhammad Suhaili Nur Hidayah Naimah Harahap