Prediction of Dengue Hemorrhagic Fever Cases Based on Weather Parameters Using Back Propagation Neural Networks (Case Study in Pontianak City) - Rahayu, I T; Nurhasanah, N; Adriat, R (Department of Physics, Faculty of Mathematics and Natural Sciences, 2019)
Research has been conducted by predicting cases of dengue hemorrhagic fever based on weather parameters. The data used are weather parameters in the form of air temperature data, air humidity, rainfall, duration of solar radiation and wind speed as input data and data on dengue hemorrhagic fever cases as the target data. This study aims to see the confirmation of dengue hemorrhagic parameters in Pontianak. The benefit in the field of education is that students and teachers are aware of the dangers of dengue because it can cause death. The method used is back propagation neural networks with the best network architecture in predicting cases of dengue hemorrhagic fever are [50 40 30 1] and binary sigmoid activation function, bipolar sigmoid and linear function. The activation function will determine whether the signal from the neuron input will be forwarded to other neurons and is also used to determine the output of a neuron. Network training correlation value is 0.9995 (very strong correlation) with MSE 0.0001 and network testing is 0.9325 (very strong correlation) with MSE 1.61. Determination coefficient serve as accuracy with values obtained is 0.85, which means that 85% of weather parameters can be used as input in predicting the incidence of dengue hemorrhagic fever in Pontianak City.