<![CDATA[Hydrometeorological floods hit Aceh on November 26, 2025, causing severe damage in several areas in Aceh. The central government, through the Regional Disaster Management Agency (BPBD), issued a decree on rapid flood management, enabling the Aceh Regional Disaster Management Agency (BPBD) to quickly collect data for post-flood aid distribution. However, many problems arose in post-flood aid distribution due to inaccurate targeting and numerous victims complaining that they had not received aid from the government. BPBD's process of determining recipients often faces inaccuracies due to the large volume of data and inefficient processing. Therefore, a classification model is needed to help more accurately and objectively identify issues related to post-flood aid recipient data. This study applies the Decision Tree algorithm to classify aid recipient data. The dataset used is a table with attributes such as age, income, number of dependents, damage conditions, marital status, and home ownership. The data was processed using Python and the scikit-learn library to build a classification model. The test results show a model accuracy of 98.53%. Evaluation metrics such as precision, recall, and F1-score indicate that the model is reliable in determining eligibility for aid recipients. This system supports a more transparent and efficient selection process.]]>
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