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All Journal Applied Information System and Management Jurnal Teknik Informatika (JUTIF)
Kadir, Nurchaerani
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Computer Science & IT Languange, Linguistic, Communication & Media

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Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method Kadir, Nurchaerani; Faisal, M.; Kurniawan, Fachrul
Applied Information System and Management (AISM) Vol 7, No 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.38466

Abstract

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.
Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method Kadir, Nurchaerani; Faisal, M.; Kurniawan, Fachrul
Applied Information System and Management (AISM) Vol. 7 No. 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.38466

Abstract

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.
Comparative Analysis of the Performance of Random Forest and CatBoost for Air Quality Prediction Based on Meteorological Factor Nirsal, Nirsal; Kadir, Nurchaerani
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5412

Abstract

Air quality in urban centers such as Tangerang City has become an increasingly urgent issue due to the expansion of industrial activities, rapid population growth, and rising vehicle emissions. As a key city within the Greater Jakarta metropolitan area, Tangerang is highly vulnerable to air pollution caused by human activities and varying meteorological conditions. This study aims to assess the performance of two machine learning algorithms, Random Forest and CatBoost, in predicting air quality in Tangerang under two scenarios: models that incorporate meteorological factors and models that exclude them. The dataset includes concentrations of key air pollutants alongside meteorological variables such as temperature, humidity, and wind speed. Model performance was evaluated using MAE, MSE, RMSE, and R². The findings indicate that both algorithms perform excellently when meteorological variables are included. Random Forest achieved an MAE of 0.0099, MSE of 0.000309, RMSE of 0.0152, and an R² of 0.9931, slightly outperforming CatBoost, which recorded an MAE of 0.0135, MSE of 0.000419, RMSE of 0.0170, and an R² of 0.9907. Excluding meteorological variables decreased accuracy for both models, with Random Forest reaching an R² of 0.9519 and CatBoost 0.9487. These results underscore the importance of temperature, humidity, and wind speed in enhancing predictive accuracy. Notably, this study introduces a comparative evaluation of machine learning models in a unique urban context, providing new insights into how meteorological factors influence air quality predictions. The study contributes to the development of adaptive air quality prediction models, supporting sustainable environmental management planning in Tangerang City.
Co-Authors Fachrul Kurniawan M. Faisal Nirsal