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Ramadhani, Irfan Wahyu
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Computer Science & IT

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Forecasting Air Quality Indeks Using Long Short Term Memory Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7402

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.
Co-Authors Guruh Fajar Shidik Ratmana, Danny Oka Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Winarsih, Nurul Anisa Sri