APPLICATION OF THE BACKPROPAGATION METHOD TO PREDICT RAINFALL IN NORTH SUMATRA PROVINCE

Natural disasters are to blame for the high level of community loss. This is due to the
community's lack of information about potential disasters around them. As a result, public
understanding of disaster response is extremely low. As a result, weather information is critical
for the smooth operation of human activities and activities, such as determining the amount of
rainfall. The goal of this research is to identify the best model for predicting rainfall in North
Sumatra Province and to forecast rainfall trends for the coming year. The rainfall time series
data used in this study were collected from six stations in North Sumatra Province over the last
ten years, including the Sibolga Meteorological Station, Aek Godang Meteorological Station,
and Silangit Meteorological Station. Backpropagation is used in this study. Backpropagation
is one of the methods used in artificial neural networks, which are usually divided into three
layers: an input layer, a hidden layer, and an output layer connected by weights. During the
training stage, the learning rate, iteration, and the number of nodes in the hidden layer were
all tested. Following the training process, the best model will be used for testing. The best model
was obtained using rainfall data from North Sumatra Province, with an optimal iteration of
1000 iterations, an optimal learning rate of 0.1 in the learning rate trial, and the best number
of hidden 5 nodes. During the testing, the MSE values were 0.047 and 0.022, respectively, and
the MSE squared value was 0.0022 and 0.00049.