PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK PENGELOMPOKAN WILAYAH RAWAN BANJIR - Jahrotun Khomsiyah; Alif Ramdhani; Ade Feby Damayanti; Dede Rohman (JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer, 2021)
The city of Cirebon is one of the cities that often experiences floods caused by very high rainfall and people often throw garbage in the sewers and into the river so that the river becomes clogged and overflows. To classify flood-prone areas in the city of Cirebon, an algorithm is needed. Therefore, the method used in this research is the K-Means Clustering Algorithm method. The data was processed using the RapidMiner application with this clustering stage producing 5 clusters consisting of Cluster 0 totaling 8 items, Cluster 1 totaling 15 items, Cluster 2 totaling 33 items, Cluster 3 totaling 10 items, and Cluster 4 having 32 items. The results of this study indicate that the K-Means Clustering Algorithm can group flood-prone areas in the city of Cirebon.