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All Journal Indonesian Journal of Applied Technology and Innovation Science
Nurazizah Nurazizah
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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PM 2.5 Prediction Using the Long Short-Term Memory Algorithm Syaid El Hasyim; Nurazizah Nurazizah; Muhammad Yudha Pratama; Umairah Rizkya Gurning; Batrisia Khairunnisa
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 2 (2025): IJATIS August 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i2.1779

Abstract

Air pollution poses a serious threat to human health and the environment, with far-reaching impacts on various aspects of life. Among its most harmful components is particulate matter less than 2.5 micrometers in diameter (PM2.5), which contributes significantly to degraded air quality. Accurate prediction of PM2.5 concentrations is crucial for public health protection and policy-making. This study employs the Long Short-Term Memory (LSTM) algorithm, a deep learning method well-suited for modeling large, complex, and time-dependent datasets, to forecast PM2.5 levels in Delhi, India. The dataset comprises daily records from January 1, 2015, to July 1, 2020. The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 25.22%, indicating moderate predictive accuracy. These results demonstrate that the LSTM algorithm can serve as an effective tool for forecasting PM2.5 concentrations, providing valuable insights for air quality management and environmental planning.
Co-Authors Batrisia Khairunnisa Muhammad Yudha Pratama Syaid El Hasyim Umairah Rizkya Gurning