IoT-based Spatiotemporal Smart Air Quality Monitoring and Analysis System with a Machine Learning Approach

Authors

  • Eddy Prasetyo Nugroho Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia https://orcid.org/0000-0001-6453-2223
  • Ani Anisyah Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Yogi Prasetyo Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Muhammad Nur Yasin Amadudin Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Jason Rafif Pangestu Suryoatmojo Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Franklin Impianro Turnip Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Jidan Abdurahman Aufan Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia
  • Deva Shofa Al Fathin Program Studi Ilmu Komputer, FPMIPA, Universitas Pendidikan Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v11i2.1809

Keywords:

Internet of Things, Pollution, Spatial-temporal, Weekdays, Weekend

Abstract

Air quality plays an important role in the sustainability of life, especially for humans. Human activities, such as industry and motorised vehicles, have increased air pollution, particularly fine particulates (PM1.0, PM2.5, PM10) and carbon monoxide (CO), which have serious impacts on health. In Bandung City, the Environmental Agency recorded high levels of pollution, so a real-time air quality monitoring system is needed. This research aims to develop an Internet of Things (IoT)-based air pollution monitoring system in various locations in Bandung, focusing on spatial-temporal analysis during weekdays and weekends. The IoT sensors used are capable of detecting pollutants such as PM1.0, PM2.5, PM10, and CO, thus enabling continuous air quality monitoring. Based on the average Air Pollution Standard Index (ISPU) scores, the monitored locations show that CO pollution levels are higher than PM. Besides, PM particulates and CO are dominant on weekdays. The result of model prediction between LSTM and GRU model is GRU model having low value based on RSME and MRE indicator and making it a better model.  The system was successfully designed and implemented, allowing air quality data and analysis results to be accessed in real-time through a website that is easy to use by the public.

 

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Published

2026-10-07

Issue

Vol. 11 No. 2 (2026)

Section

Article

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Copyright (c) 2026 Eddy Prasetyo Nugroho, Ani Anisyah, Yogi Prasetyo, Muhammad Nur Yasin Amadudin, Jason Rafif Pangestu Suryoatmojo, Franklin Impianro Turnip, Jidan Abdurahman Aufan, Deva Shofa Al Fathin

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License