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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Emerging Science and Engineering
Barid, Aziz Jihadian
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Computer Science & IT Engineering Environmental Science

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Journal : Journal of Emerging Science and Engineering

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Hyperparameter optimization for hourly PM2.5 pollutant prediction Barid, Aziz Jihadian; Hadiyanto, H.
Journal of Emerging Science and Engineering Vol. 2 No. 1 (2024)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2024.e15

Abstract

Air pollution, particularly the presence of Particulate Matter (PM) 2.5, poses significant health risks to humans, with industrial growth and urban vehicle emissions being major contributors. This study utilizes machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, to predict PM2.5 levels. A dataset from Kaggle consisting of PM2.5 and other pollutant parameters is preprocessed and split into training and testing sets. The models are trained, evaluated, and compared using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Additionally, hyperparameters are applied to optimize the models. Results show that SVM with hyperparameters performs better, indicating its potential for accurate air quality prediction. These findings can aid policymakers in implementing effective pollution control strategies.
Co-Authors Dinar Mutiara Kusumo Nugraheni H Hadiyanto Wiryadinata, Doni