Disaster is a series of events that threaten and disrupt human life caused by natural factors, non-natural factors and human factors themselves. Therefore, disasters cause casualties, environmental damage, property losses, and psychological impacts. In this study will be discussed about the prediction of the number of victims affected by the disaster, either died, lost, injured, suffered or displaced. Data sources were obtained by the National Disaster Management Agency and the Indonesian Central Statistics Agency. The method used to predict is the Incremental Sequential Order method. This method is one part of the Artificial Neural Network method. With this method, network architecture patterns will be established to predict the number of victims affected by the disaster for years to come. The network architecture models used are 4-5-1, 4-10-1, 4-5-10-1, 4-10-20-1 and 4-15-30-1. Of the five models, the best models will be obtained, namely 4-15-30-1 with an accuracy rate of 80%. With this architectural model, predictions will be made on the number of victims affected by the disaster for years to come.
K-Means is a method that is well-known for its ability to handle large datasets, but is often stuck in a local optima state. This issue happens because K-Means generally uses random numbers to serve as the center point (centroid) of each cluster, and places each instance based on the proximity of the distance using Euclidean Distance. Hence, the concept of density parameter was developed, which tries to determine the ideal centroid based on the determination of several grid points in each existing cluster. The concept is known as Grid Mapping. In simple terms, Grid Mapping K-Means is a method in which each cluster is divided into several grid points, and then the centroid is randomly determined from each existing grid point. Further, the grid point with the largest number of instance is used as the initial centroid of each cluster. However, this method certainly has a weakness, since there is a possibility that the initial centroid on the generated grid point is not the best initial centroid. Therefore, this study was conducted to test the determination of initial centroid by Grid Mapping K-Means.
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