<![CDATA[Poverty remains major social issue in Indonesia, so an analysis of its causal factors is necessary to assist the government in making informed decisions. This study aims to classify poverty levels using the C4.5 algorithm by utilizing a dataset containing various socio-economic indicators such as education level, unemployment, and per capita expenditure. The research stages began with data collection and cleaning, transformation, splitting the dataset into training and testing data, and building the classification model. The results show that the C4.5 algorithm is capable of classifying poverty levels effectively and producing clear decision tree patterns. Based on the generated model, the per capita expenditure variable was identified as the dominant factor most influencing poverty status in a region. This model is expected to serve as a basis for formulating more targeted policies to reduce poverty levels in Indonesia.]]>
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