<![CDATA[Flood disasters are a natural phenomenon that often occur in various regions in Indonesia, including cities such as Banjarnegara, Boyolali and Karanganyar. Floods have a significant impact on the economy, social and environment. Therefore, proper understanding and analysis of the factors that cause flooding is very important for effective mitigation efforts. This research uses a decision tree algorithm to analyze meteorological data including temperature, wind speed, humidity and rainfall from various cities in Indonesia. This data includes normal to hot temperature conditions, slightly calm wind speed to light gusts, high to moderate humidity, and extreme to very high rainfall. The results of the analysis show patterns and relationships between these variables and flood events. The decision tree algorithm is used to build a prediction model in the form of a decision tree, which makes interpretation and decision making easier. This research aims to identify the main factors that contribute to flooding and develop a prediction model that can be used to improve preparedness and response to flood disasters. By understanding the patterns and factors that cause flooding, it is hoped that more effective mitigation measures can be implemented to reduce the risk and impact of thisdisaster.]]>
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