Noprianto, Nobuo Funabiki, Htoo Htoo Sandi Kyaw, Komang Candra Brata, I Nyoman Darma Kotama, Yan Watequlis Syaifudin, Alfiandi Aulia Rahmadani
—Air quality significantly affects quality of life (QoL). Air pollutants can cause serious health issues such as sick building syndrome (SBS). To identify them and apply coun-termeasures, air quality monitoring is required. However, since monitoring locations are often scattered and temporary, a low-cost and portable system is needed. In this paper, we present a portable air quality monitoring system that is equipped with multiple sensors and is integrated with the SEMAR IoT application server, providing data synchronization, AI-based analysis, and web-based visualization. The system is evaluated through cedar pollen monitoring at Okayama University, Japan, using a PM10 sensor. Measurement results are validated against public data. Besides, several machine learning models are compared for pollen density prediction, where random forest achieves the best performance, highlighting the impact of lag and window parameters. © 2026, International Association of Engineers. All rights reserved.
Department of Information and Communication Systems, Okayama University, Okayama, Japan; Department of Information Technology, State Polytechnic of Malang, Malang, Indonesia
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