<![CDATA[Household poverty is a social issue that requires accurate identification to ensure that poverty alleviation programs are implemented effectively and targeted appropriately. Wotan Ulumado Subdistrict, East Flores Regency, has diverse socioeconomic characteristics; therefore, a data-driven method is needed to classify household poverty levels. This study aims to analyze the socioeconomic factors associated with household poverty and apply the Naive Bayes method to classify households according to their poverty status. The data were collected through field observations, interviews, and a literature review. The variables examined included household income, number of dependents, the educational level of the household head, housing conditions, and asset ownership. The dataset consisted of 360 training records and 28 testing records. The classification process was conducted by calculating the prior, likelihood, and posterior probabilities for each poverty category. The classification categories comprised poor, moderately poor, and non-poor households. Evaluation using a confusion matrix showed that 26 out of 28 testing records were correctly classified, resulting in an accuracy rate of 92.86%. These findings indicate that the Naive Bayes method performs well in identifying household poverty levels based on socioeconomic indicators. The classification results can further be presented in the form of tables, graphs, and maps to illustrate the distribution of poverty levels and support local government decision-making in determining priority households and areas for poverty alleviation programs in Wotan Ulumado Subdistrict.]]>
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