Analysis Prediksi Wilayah Rawan Banjir dengan Algoritma K-Means
-
Muhammad Makmun Effendi
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Inka Inka Universitas Pelita Bangsa, Bekasi, Indonesia
- Arif Siswandi Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
Along with the high amount of rainfall in Bekasi -West Java, floods have started to inundate several areas of Bekasi , one of the causes is the high rainfall factor. According to (Regional Disaster Management Agency) BPBD, the most flood points are in the Bekasi area, causing several activities of the surrounding community to be disrupted, transportation hampered, and also the emergence of disease problems such as skin diseases, diarrhea, and so on. The problem of flooding is a shared responsibility that requires a solution. also the role of technology to help facilitate the provision of information to the public regarding flood-prone areas in the Bekasi area. One technique that can be used is using the K-Means Clustering Algorithm to group flood-prone areas. The flood dataset was processed using the RapidMiner application, for the dataset taken to carry out this analysis from January to December 2022, there were 24 data from areas affected by flooding from various sub-districts and villages in the city of Bekasi. The results of the research produced 3 clusters, namely, the high flood, medium flood and low flood categories, which received a Davies Bouldin index value of -0.452.
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Pages: 697-703
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