DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY
DOI:
https://doi.org/10.33480/jitk.v10i4.6429Keywords:
deep learning, electric vehicles, gated recurrent units, optimation, predictionAbstract
The development of electric vehicles is an important innovation in reducing greenhouse gas emissions while reducing dependence on fossil fuels. The main problem in developing electric vehicles is the lack of adequate infrastructure. Inaccurate predictions regarding the number of electric vehicles hinder adequate infrastructure planning and development. This research proposes the use of the Gated Recurrent Units (GRU) algorithm to improve the accuracy of electric vehicle population predictions by carrying out GRU parameter transformations. This parameter transformation involves searching and adjusting the parameters of the GRU model in more depth to increase its ability to handle uncertainty in electric vehicle population data. After carrying out the training and testing process, the result was that the hyperparameter model using RandomizedSearchCV was the best model because it had the highest accuracy compared to other models tested with a combination of GRU_unit 64 and 128, dropout 0.5 and 0.6, batch size 64 and the number of epochs was 100 which had MAE results: 257.94, MSE: 66655.087, RMSE: 258.176, and Accuracy of 100%.
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Copyright (c) 2025 Jeni Sugiandi, Solikhun Solikhun, Anjar Wanto
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