<![CDATA[Electricity has become an essential part of our daily lives. As technology has rapidly developed, many modern activities and devices have become highly dependent on electricity. The more electricity that is used, the higher the monthly cost. This cost is influenced by usage patterns and various uncertain factors. Fuzzy logic is one approach that can be used in decision support systems in the face of uncertainty like this. This study aims to apply the Mamdani and Sugeno fuzzy methods based on house building area, number of electronic devices, number of family members, and income to determine which method more accurately predicts household electricity consumption costs based on the mean absolute percentage error (MAPE) value. Data for this study were obtained through questionnaires and interviews with residents of Margorejo Village. Data processing yielded a MAPE value of 12.3% for the Mamdani method and a MAPE value of 9.9% for the Sugeno method. Based on these results, the MAPE value for the Sugeno method is smaller than that for the Mamdani method. Therefore, it can be concluded that the Sugeno method is more accurate for predicting household electricity consumption costs in Margorejo Village.Keywords: Mamdani Method, Sugeno Method, MAPE.]]>
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