Optimisasi dalam Pengelompokan Zona Bahaya setelah Bencana Gempa

Abstract

There are a large number of approaches to clustering problems, including opti- mization - based methods involving mathematical programming models to develop efficient and meaningful clustering schemes. Grouping is one of the data labeling techniques. K-means clustering is a partition clustering algorithm that starts by selecting k representative points as the initial centroid. Each point is then as- signed to the nearest centroid based on the selected specific proximity measure. This writing is focused on the grouping of hazard zones after optimizing the Sum of Square Error (SSE) using the elbow method. This study consists of five steps, namely (1) data collection, (2) calculating the value of Sum of Square Error (SSE) and (3) optimization using the elbow method (4) Performing a K-mean clustering algorithm on 8 variables, namely magnitude (SR), depth, death toll, non-death victim, heavy damage to public facilities, light damage to public facilities, affected areas and less affected areas.