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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) The Indonesian Journal of Computer Science
Achmad Ridwan
Universitas Prima Indonesia
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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IoT-Based Stress Monitoring Using CNN for HRV-GSR Analysis Segono, Yohandres; Hutagalung, Elia Yose Mayal; Simbolon, Harly Gumanti; Us, Nurul Iman; Ridwan, Achmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15671

Abstract

Stress has become a major global health concern affecting both physical and mental well-being. Conventional stress assessment methods rely on subjective self-reports that cannot capture real-time physiological changes. Existing systems are often limited to controlled laboratory environments or depend on traditional machine learning approaches requiring extensive manual feature engineering. This study aims to develop an Internet of Things–based stress monitoring system using deep learning to enable objective, continuous, and practical real-world stress detection.  The system incorporates wearable sensors using an ESP32-DevKit V1 microcontroller, a MAX30102 photoplethysmography sensor, and a Grove-GSR module for real-time acquisition of Heart Rate Variability and Galvanic Skin Response signals. A dual-branch Convolutional Neural Network architecture processes preprocessed HRV and GSR time-series data to automatically learn discriminative features without manual feature engineering. Data were collected from 30 participants, resulting in 8,100 labeled samples across four stress levels. The proposed CNN model achieved 91.3% classification accuracy, outperforming baseline machine learning models such as Support Vector Machine (78.4%), Random Forest (81.7%), and XGBoost (84.3%). Real-time system evaluation demonstrated an average latency of 1.47 seconds and battery endurance exceeding 13 hours, confirming the feasibility of continuous wearable stress monitoring. The integration of IoT infrastructure with deep learning provides an effective framework for physiological stress assessment, offering potential applications in preventive healthcare, workplace health management, and personalized mental-wellness monitoring.
Tren Terkini dan Tantangan dalam Implementasi IoT untuk Layanan Kesehatan: A Systematic Literature Review Simangunsong, Putra Torang; Sihombing, Yehezkiel; Ridwan, Achmad
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10317

Abstract

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.
Co-Authors Agung Prabowo Amir Saleh Duandes, Alfonso Fadhillah Azmi Fadhillah Azmi Fadhillah Azmi, Fadhillah Gibran, M Khalil Hutagalung, Elia Yose Mayal Lenny, Lenny M Khalil Gibran M. Khalil Gibran Nugraha, R. Ferdy Akbar Segono, Yohandres Sihombing, Yehezkiel Simangunsong, Putra Torang Simbolon, Harly Gumanti Susena, Arya Tindaon, Novendy Alberto Will Us, Nurul Iman Violyn, Violyn Yennimar, Yennimar