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Joni Siagian
Akper gita matura abadi kisaran
Computer Science & IT Education Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Comparing PSO and ACO for XGBoost Heart Rate Classification in IoT Monitoring Joni Siagian; Afrizal Hasan; Khairunnisa Khairunnisa
SISTEMASI Vol 15, No 10 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i10.6729

Abstract

Advances in Internet of Things (IoT) technology have transformed health monitoring systems, making them more adaptive and capable of acquiring real-time data. Heart rate, measured in beats per minute (BPM), is an important indicator of health status; however, BPM data generated by IoT devices is generally presented as numerical values without automatic classification of normal or abnormal conditions. This study aims to develop an IoT-based heart rate pattern classification system using the Extreme Gradient Boosting (XGBoost) algorithm, optimized with two metaheuristic algorithms Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), and to compare their performance characteristics. The study employs a quantitative experimental approach, beginning with real-time BPM data acquisition using a MAX30102 sensor and an ESP32-C3 Mini microcontroller from 40 participants, followed by data preprocessing, the development of an XGBoost model, parameter optimization using PSO and ACO, and model performance evaluation. The results show that both algorithms deliver equally good classification performance. PSO successfully identified an optimal threshold range of 68.44–97.82 BPM with an inference complexity of O(1), making it very fast during the prediction stage, while ACO excelled in training convergence speed and feature reduction effectiveness by using only 30% of the total features. The main contribution of this research is a comprehensive comparative analysis of the characteristics of PSO and ACO in terms of computational efficiency, optimization speed, inference complexity, and feature selection effectiveness, resulting in recommendations for selecting the appropriate optimization method to meet the implementation needs of IoT-based health monitoring systems, whether for real-time devices or cloud-based systems.
Co-Authors Afrizal Hasan Amelia Dini Anggraini Silalah Amelia Dini Anggraini Silalahi Amelia Dini Angraini Silalahi Efi Irwansyah Pane Febi Annisa Lubis Hafsanah Hafsanah Nikmahtullah Isbatul Khoiroh Khairunnisa Batubara Khairunnisa Khairunnisa Kurnia Wati M. Arif Simangunsong M. Hafinawa Husaini Muhammad Arif Simangungsong Naila Khalis Nasywa Azra Zeina Sirait Novia Elida Yanti Gurning Novia Elidayanti Gurning R Sri Rezeki R. Sri Rezeki Salsa Billa Sinta Juli Asmara Sri Julita Sri Legawati Syahidul Haq Tia Ariska Panjaitan Umi Kalsum Wahyu Agustina Yuriansah Yuriansyah