OPTIMASI LONG SHORT TERM MEMORY DENGAN ADAM MENGGUNAKAN DATA UDARA KOTA DKI JAKARTA
Air is an important element for human life in the process of breathing. Economic growth and urbanization that occur in large urban areas have the potential to increase the use of electricity, water, and petroleum energy. This results in air pollution which causes poor air quality in big city areas s...
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2022-07-04.
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| 520 | |a Air is an important element for human life in the process of breathing. Economic growth and urbanization that occur in large urban areas have the potential to increase the use of electricity, water, and petroleum energy. This results in air pollution which causes poor air quality in big city areas such as Jakarta. In Indonesia, five parameters are used as pollutants that cause air pollution, these pollutants include Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Dust Particles (PM10), Ozone (O3), and Carbon Monoxide (CO). These five pollutant substances become benchmarks for determining the air level in the Air Pollution Standard Index (ISPU). The Long Short Term Memory method is used in this study as a model used in processing time series data. The LSTM model is used for prediction of air quality with minimum computational error. The use of the LSTM model by using the Adam Optimizer to optimize the value of each layer to produce accurate predictions. Prediction results with MAPE accuracy on PM10 parameter is 4,37%, SO2 parameter is 5,02%, CO parameter is 18,50%, O3 parameter is 5,23%, and NO2 parameter is 37,28%. | ||
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