<![CDATA[Poverty is a global problem that has occurred in various countries with various impacts. Poverty conditions are characterized by the inability of a person or household to meet the basic needs of life. Socio-economic problems, such as poverty, can be handled using machine learning, one of which is classification. The classification of households based on poverty criteria is expected to assist the government in preparing programs that are right on target. K-Nearest Neighbor is one of the easy-to-use classification algorithms. this classification is based on the closest neighborliness. The problem that can be experienced when classifying is if the data used is imbalanced. The data imbalance will causing the classification process to focus more on the majority class. SMOTE and ADASYN are used to solve the problem of imbalanced data. This study resulted in the addition of SMOTE and ADASYN to imbalanced data can improve classification performance, especially on the G-mean value. G-mean is a performance measure that is widely used in the case of imbalanced data. The result of this study is that SMOTE can increase the G-mean value to 58.5%, while ADASYN is 57.3%. Therefore, it can be concluded that SMOTE-KNN is the best classification model for household poverty classification.]]>
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