Disaster Mitigation Efforts Using K-Medoids Algorithm and Bayesian Network

Disaster mitigation is a series of efforts to reduce disaster risk. One of the disaster mitigation efforts is the supervision of the implementation of spatial planning. Knowing the level of damage to buildings in a region in the event of a disaster can supervise the implementation of spatial planning. To predict the level of damage to buildings in an area, we can use the Bayesian network Model. Bayesian network is an extension of Naive Bayes. There are several types of Bayesian networks based on the variable type, namely discrete Bayesian network, continuous Bayesian network, and hybrid Bayesian network. A discrete Bayesian network is a Bayesian network model in which all the variables involved are discrete. Therefore, if there is a continuous variable, it is necessary to discretize the variable. In this paper, modifications are made to the algorithm commonly used in the clustering process to be used in the discretization process. The algorithm used is the K-Medoids algorithm, where this algorithm uses existing data as a representative of the cluster center. Then, the Bayesian network model and the K-Medoids algorithm were used to determine the level of damage to buildings due to the earthquake that occurred in West Sumatra in 2009. From 25,000 house damage data used in this study, we obtain an accuracy rate is 95.17%.