Flood Prediction with Naive Bayes Method
Keywords:
K-Means, Naïve Bayes, Flood, DKJ, Prediction.Abstract
The problem of traffic jams in the Special Region of Jakarta (DKJ) includes various kinds of traffic problems in Jakarta, including traffic jams and flooding. According to the Journal of Sustainable Development Education and Environment, land is lacking for housing, industrial development, and urbanization. The development of an urban area can be influenced by the rapid rate of urbanization, which requires a lot of land, while land in urban areas is relatively minimal. In the literature journal related to the research that will be studied using the Naïve Bayes Algorithm and K-Means Clustering, it can be concluded that it can be used to predict the 2025 flood. The above research results are provided in the form of diagrams, codes, and dashboards, which have a value of results. From the results of clustering using the K-Means method, a water level prediction of 0.96 x 100% = 96% of the data in clustering is very accurate, with an average accuracy of 97% when there is a flood. From the research conducted by the researcher, it can be concluded that data mining is processed through an algorithm. The use of the algorithm affects the data processing results. The researcher used the Naïve Bayes based on the Reference Journal related to the best algorithm in data mining processing. The results of data mining processing are in the form of a curve that can be presented to readers to estimate whether a flood disaster will occur.
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