Detection of Clustering of Flood Hazard Data using K-Means Algorithm and Fuzzy C-Means

Saruni Dwiasnati ,Yudo Devianto

International Conference on Industrial Revolution for Polytechnic Education
Vol. 1 No. 1 (2019): International Conference on Industrial Revolution for Polytechnic Education

This paper discusses predicting the dangers of flooding that occurred in the previous year in order to produce knowledge about which areas are more likely to experience the danger of flooding in order to make estimates in the future. The variables used in this study use the flood day, the period of flooding, longitude and latitude flooding. The algorithm used in this study uses the K-Means algorithm and Fuzzy C-Means. This study uses Rapidminner as a tool to assess data calculated to produce a model. The application used for this study uses the PHP programming language, and the database used is MySQL. The results of this grouping are small potential flood areas, moderate potential flood areas and large potential flood areas. The cluster center is obtained from several iterations so that it can produce a better cluster center for the determination of areas potentially affected by floods in the following year.