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Journal
SINTA 2
Predictive Modeling of Urban Air Quality Using Machine Learning
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 2460-7258, 1978-1520
Published 2026-07-31
Publisher IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language eng
Abstract
Air pollution is a critical environmental concern adversely affecting public health in urban areas worldwide. Accurate prediction of air quality enables timely intervention by environmental agencies and policymakers. This study presents a comprehensive comparative analysis of ensemble machine learning methods for predicting urban air quality using the publicly available UCI Air Quality Dataset, containing 9,358 hourly instances of chemical sensor measurements collected in a heavily polluted Italian city from March 2024 to February 2025. Five algorithms were evaluated: Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and CatBoost, against Ridge Regression and SVR baselines. A systematic pipeline encompassing preprocessing, temporal feature engineering, Bayesian hyperparameter optimization via Optuna, and time-series cross-validation was implemented. Results demonstrate XGBoost achieved the best performance with RMSE = 2.14, MAE = 1.58, and R² = 0.9312. SHAP-based feature importance analysis revealed CO(GT) lag features, C6H6(GT), and NOx(GT) as the most influential predictors.
Collaboration network of the authors
22 authors ·
53 co-authorship links
· 1 across different organisations
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Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.
Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">Predictive Modeling of Urban Air Quality Using Machine Learning</dc:title>
<dc:creator>Dahlan, Akhmad</dc:creator>
<dc:creator>Fatta, Hanif Al</dc:creator>
<dc:creator>Farida, Lilis Dwi</dc:creator>
<dc:creator>Utama, Hastari</dc:creator>
<dc:creator>Pristyanto, Yoga</dc:creator>
<dc:subject xml:lang="en-US">Computer Science</dc:subject>
<dc:subject xml:lang="en-US">Air Quality Prediction; Ensemble Methods; XGBoost; SHAP; UCI Dataset</dc:subject>
<dc:description xml:lang="en-US">Air pollution is a critical environmental concern adversely affecting public health in urban areas worldwide. Accurate prediction of air quality enables timely intervention by environmental agencies and policymakers. This study presents a comprehensive comparative analysis of ensemble machine learning methods for predicting urban air quality using the publicly available UCI Air Quality Dataset, containing 9,358 hourly instances of chemical sensor measurements collected in a heavily polluted Italian city from March 2024 to February 2025. Five algorithms were evaluated: Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and CatBoost, against Ridge Regression and SVR baselines. A systematic pipeline encompassing preprocessing, temporal feature engineering, Bayesian hyperparameter optimization via Optuna, and time-series cross-validation was implemented. Results demonstrate XGBoost achieved the best performance with RMSE = 2.14, MAE = 1.58, and R² = 0.9312. SHAP-based feature importance analysis revealed CO(GT) lag features, C6H6(GT), and NOx(GT) as the most influential predictors.</dc:description>
<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
<dc:contributor xml:lang="en-US">Universitas Amikom Yogyakarta, Faculty of Computer Science</dc:contributor>
<dc:date>2026-07-31</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/117546</dc:identifier>
<dc:identifier>10.22146/ijccs.117546</dc:identifier>
<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 3 (2026): July; 295-306</dc:source>
<dc:source>2460-7258</dc:source>
<dc:source>1978-1520</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/117546/44943</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
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APA 7
Dahlan, A., Fatta, H. A., Farida, L. D., Utama, H., & Pristyanto, Y. (2026). Predictive Modeling of Urban Air Quality Using Machine Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 20(3), 295–306. https://doi.org/10.22146/ijccs.117546
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IEEE
A. Dahlan, H. A. Fatta, L. D. Farida, H. Utama, and Y. Pristyanto, “Predictive Modeling of Urban Air Quality Using Machine Learning,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 20, no. 3, pp. 295–306, 2026, doi: 10.22146/ijccs.117546.
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