Classification and Prediction of Air Quality in Yogyakarta City Using Long Short-Term Memory Algorithm
https://doi.org/10.22146/juliet.v6i2.106999 Novita Bayu Permatasari(1), Hidayat Nur Isnianto(2*)
(1) Department of Electrical Engineering and Informatics, Universitas Gadjah Mada
(2) Department of Electrical Engineering and Informatics, Universitas Gadjah Mada
(*) Corresponding Author
Abstract
Air quality in Yogyakarta City is declining along with the increase in air pollution caused by human activities, such as transportation and industry, thus having a serious impact on health and the environment. Some of the parameters are PM10, SO2, Com O3, and NO2 which are based on ISPU (Air Pollution Standard Index) data from the Yogyakarta City Environment Agency from 2020 to 2023. In this study, classification and prediction were carried out using the LSTM (Long Short-Term Memory) algorithm to predict air quality standards with five pollution parameters of PM10, SO2, CO, O3, and NO2. The results of this study on the performance graph showed that the model was likely to be overfitted in some phases of training, although the simulation results on the random data showed consistency in the prediction with the "Good" category for air quality. So it can be concluded that this study using the LSTM (Long Short-Term Memory) model is able to classify and predict air quality effectively with results that are almost in accordance with the actual data and this study will run better with several improvements such as the use of additional parameters such as PM2.5 and increased model accuracy for optimal results.
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DOI: https://doi.org/10.22146/juliet.v6i2.106999
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