Abstract
Decision Support Systems (DSS) have become essential tools in complex and data-driven decision-making processes. In the context of identifying beneficiaries for the Program Keluarga Harapan (PKH), the fuzzy Mamdani method has proven effective in addressing uncertainty and complexity by evaluating the eligibility of recipients. The fuzzy Mamdani method employs fuzzy logic principles to handle uncertainty by establishing connections between input variables (e.g., household income, number of dependents, education level) and output variables (PKH recipient status). Fuzzification and defuzzification processes enable the mapping of vague input values to comprehensible output values. This study discusses the implementation of the fuzzy Mamdani method within a DSS for determining PKH recipients. Real-world data collected from potential PKH recipient households is utilized to develop the fuzzy Mamdani model. Model development steps encompass defining input variables, membership functions, fuzzy rules, and the defuzzification mechanism. Consequently, the utilization of the fuzzy Mamdani method within a DSS for PKH accuracy 87%, recall 91% and precision 90%. However, further research and the development of more intricate models may be necessary to optimize the performance of this method in broader and diverse scenarios.
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