CLUSTERING THE POTENTIAL RISK OF TSUNAMI USING DENSITY-BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE (DBSCAN)

Muhammad Tanzil Furqon, Lailil Muflikhah

Abstract


Tsunami is one of the deadliest natural disaster that causing devastating property damage and loss of life. Therefore, this triggers many scientist to do researches in tsunami mitigation disaster, such as analyzing the potential risks caused by tsunami. The process of analyzing the potential risk caused by tsunami can be done by grouping the data of tsunami based on characteristics of the previous tsunami events. DBSCAN (Density-based Spatial Clustering of Application with Noise) is a popular clustering method and can be used to do the task. The algorithm do the clustering processes using density-based concept that able to detect outlier/noise and clusters irregular shapes. It was proved in this research where the evaluation method using Silhouette Coefficient on the DBSCAN clustering result gave highest value 0.96056649 for ε and minPts value of .1 and 0.1.


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References


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DOI: http://dx.doi.org/10.21776/ub.jeest.2016.003.01.1

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