Prediksi Kejadian Banjir Di Kota Bandar Lampung Menggunakan Jaringan Syaraf Tiruan - Ramadhan Nurpambudi; Eka Suci Puspita Wulandari; RZ. Abdul Aziz (LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO, 2023)
The city of Bandar Lampung is currently experiencing seasonal flooding which occurs almost every year, resulting in significant losses. Floods recorded by BNPB in the last 10 years there were 16 incidents of flooding in the Bandar Lampung area. More than 14,000 people suffered, more than 500 people had to be evacuated, more than 900 houses were damaged, and 4 public facilities were damaged. To study the pattern of flood events in the past, the Artificial Neural Network Backpropagation learning method will be used which will utilize its non-linear variable learning abilities. The configuration settings for the Artificial Neural Network were carried out experimentally without any basis for assigning values, especially for the parameters of the number of hidden layers, number of neurons, and epochs used in training and variable testing. The results obtained from this study are the results of training and testing of datasets that have been carried out by ANN backpropagation are able to properly study patterns of flood events and also non-flood events in the dataset, this is evidenced by the results of high model configuration accuracy and also the results of predictive tables that able to describe actual conditions, setting the configuration model experimentally is able to produce an accuracy value of 90-100%, an average training correlation value of 0.96 and an average test correlation value of 0.89, and an average error value of 0.0089 out of 20 model configuration, and the flood prediction table are made based on the 1 best configuration with a training and testing accuracy rate of 100% with an error value of 0.00134, namely configuration model 20, the prediction table uses an average air temperature of 27˚C with 80% humidity. The prediction table is able to produce excellent flood potential results which are able to represent flood events as well as non-flood events based on the results of the dataset learning.