<![CDATA[The increasing demand for electricity in the household sector poses significant challenges to energy efficiency initiatives and environmental conservation efforts. Examining electricity usage patterns offers a pathway to uncover key determinants that influence consumption levels while formulating more effective strategies for energy management. This study attempts to evaluate electricity consumption patterns in the household sector using the K-Nearest Neighbor (KNN) algorithm. This approach is used to categorize consumption data based on attribute similarities among household units. The findings are expected to encourage more rational electricity usage practices, thereby reducing energy inefficiencies and strengthening efforts to conserve natural resources. Furthermore, the analysis aims to provide actionable insights for households to adopt sustainable habits and for policymakers to design targeted interventions that address peak demand periods and promote the use of energy-efficient technologies. By identifying specific behavioral and technological factors that contribute to high consumption, the results can serve as a basis for tailored programs aimed at minimizing waste and promoting long-term environmental management.]]>
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