| dc.description.abstract | Malaria, a disease caused by mosquitoes infected with Plasmodium parasites,
continues to be a serious global health issue. Despite efforts to reduce malaria
cases, there has been a significant increase, especially during the COVID-19
pandemic. The World Health Organization (WHO) has set a target through the
Global Technical Strategy (GTS) to substantially reduce the number of malaria
cases, but as of now, this target has not been achieved. To attain this goal and
control the spread of this disease, it is crucial to predict the total estimated malaria
cases that will occur and take appropriate preventive actions based on these
predictions. This research employs inferential statistical approaches to analyze
meteorological variables that influence total malaria cases and utilizes a deep
learning method known as the Gated Recurrent Unit (GRU) to predict malaria
occurrences for the next 12 weeks. The data used includes meteorological
parameters such as rainfall, air temperature, and wind speed. The malaria
incidence data analyzed originates from Batu Bara District, North Sumatra,
collected daily throughout the year 2020. To complete the dataset, this study will
also synthesize data for the next three years using Conditional Tabular Generative
Adversarial Networks (CT-GAN). The best-performing model achieved a training
loss with a Mean Squared Error (MSE) of 0.008 and a validation loss with an MSE
of 0.025. The model parameters used encompass a maximum of 500 epochs, 64
hidden neurons, a batch size of 8, a learning rate of 0.001, L2 regularizers at 0.01,
and the Adam optimizer | en_US |
