Penerapan Data Mining dalam Analisis Kejadian Banjir di Indonesia dengan Menggunakan Metode Association Rule Algoritma Apriori

Abstract

Floods are a natural disaster that frequently occurs in Indonesia. Disaster prevention measures and flood simulation and guidance are still less applied in other cities except for major cities, resulting in a low level of human safety. From the data obtained, many casualties and significant material losses were suffered by flood victims. Floods usually occur during the rainy season, but no one knows when and where they will happen. This study applies data mining techniques with association rules using the apriori algorithm to understand the patterns and association rules of flood events in Indonesia. The data were taken from the official website of the National Disaster Management Agency (BNPB) from 2014-2016 and analyzed using the R program. The association analysis results showed that "if there is flooding due to all survival factors, then there is a possibility of flooding in Cileunang" with support 38.7%, confidence 64.4%, and lift 1.0347319. Meanwhile, "if there is flooding in Cileunang, then there is a possibility of all surviving" with support 38.7%, confidence 64.4%, and lift 1.0347319.