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Widyarini, Liza
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Computer Science & IT

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Air Quality Prediction Using the Support Vector Machine Algorithm Widyarini, Liza; Purnomo, Hindriyanto Dwi
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.705

Abstract

Air quality is an important factor in maintaining the health and well-being of humans and the environment. To anticipate and manage air pollution, air quality prediction has become an important research topic. In this research, researchers propose using the Support Vector Machine (SVM) algorithm to predict air quality. SVM has proven to be an effective method in classification and regression, especially in the context of complex and non-linear data such as air quality data. Researchers utilized historical air quality datasets that include various parameters such as particulates, ozone, nitrogen dioxide and carbon monoxide. Experiments were conducted to compare the performance of SVM with other prediction methods, and the results show that SVM provides accurate and reliable predictions in modeling air quality.
Co-Authors Hindriyanto Dwi Purnomo