SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks

Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.