Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring

Authors

  • Ghazi Mauer Idroes Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia; Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Zahriah Zahriah Department of Architecture and Urban Planning, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Suhendrayatna Suhendrayatna Department of Chemical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Eko Suhartono Department of Medical Chemistry/Biochemistry, Faculty of Medicine, Lambung Mangkurat University, Banjarbaru 70124, Indonesia
  • Khairan Khairan Department of Pharmacy, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia; Department of Chemistry, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
  • Fitranto Kusumo Centre for Technology in Water and Wastewater, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007 NSW Australia
  • Zuchra Helwani Department of Chemical Engineering, Universitas Riau, Pekanbaru 28293, Indonesia
  • Sunarti Abd Rahman Faculty of Chemical & Process Engineering Technology, Universiti Malaysia Pahang, Lebuhraya Persiaran Tun Khalil Yaakob, 26300 Gambang, Kuantan, Pahang, Malaysia

DOI:

https://doi.org/10.60084/ljes.v1i2.99

Keywords:

Air quality index, Artificial intelligence, Data analysis, Pollutant

Abstract

Urban areas worldwide grapple with environmental challenges, notably air pollution. DKI Jakarta, Indonesia's capital city, is emblematic of this struggle, where rapid urbanization contributes to increased pollutants. This study employed the CatBoost machine learning algorithm, known for its resistance to overfitting and capability to handle missing data, to predict urban air quality based on pollutant levels from 2010 to 2021. The dataset, sourced from Jakarta's air quality monitoring stations, includes pollutants such as PM10, SO2, CO, O3, and NO2. After preprocessing, we used 80% of the data for training and 20% for testing. The model displayed high accuracy (0.9781), precision (0.9722), and recall (0.9728). The feature importance chart revealed O3 (Ozone) as the top influencer of air quality predictions, followed by PM10. Our findings highlight the dominant pollutants affecting urban air quality in Jakarta, Indonesia and emphasizing the need for targeted strategies to reduce their concentrations and ensure a cleaner and healthier urban environment.

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Published

2023-11-06

How to Cite

Idroes, G. M., Noviandy, T. R., Maulana, A., Zahriah, Z., Suhendrayatna, S., Suhartono, E., Khairan, K., Kusumo, F., Helwani, Z., & Abd Rahman, S. (2023). Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring. Leuser Journal of Environmental Studies, 1(2), 62–68. https://doi.org/10.60084/ljes.v1i2.99

Issue

Vol. 1 No. 2 (2023): November 2023

Section

Articles

License

Copyright (c) 2023 Ghazi Mauer Idroes, Teuku Rizky Noviandy, Aga Maulana, Zahriah Zahriah, Suhendrayatna Suhendrayatna, Eko Suhartono, Khairan Khairan, Fitranto Kusumo, Zuchra Helwani, Sunarti Abd Rahman

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