Robust Large-Scale Poverty Prioritization Using a-Cut Fuzzy AHP and Fuzzy WASPAS - Hauzan Hanifah Zahra; Amri Muhaimin; Sugiarto (Ali Institute or Research and Publication, 2026)
Prioritizing poverty alleviation programs remains challenging due to multidimensional indicators and uncertainty in expert judgment. This study purposes a robust decision support framework by integrating -cut Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (Fuzzy WASPAS) for large-scale poverty prioritization. The -cut mechanism was incorporated into the Fuzzy AHP weighting process to improve flexibility and robustness under uncertainty, while Fuzzy WASPAS was employed to rank 20,000 household alternatives based on 14 poverty indicators derived from DTKS and BPS Data. Sensitivity analysis was conducted using several values to evaluate ranking stability under varying confidence levels. The results demonstrate that the proposed framework produces highly stable rankings, with an average maximum rank shift of 143 positions (0.7%) and a median shift of 81 positions (0.4%) across all alternatives. Futhermore, the model achieved an average Spearman rank correlation of 0.9996, indicating strong consistency in poverty prioritization outcomes despite variations in fuzzy defuzzification parameters. The findings confirm that the integration of α-cut Fuzzy AHP and Fuzzy WASPAS provides a reliable and robust approach for evidence-based poverty targeting and social assistance allocation. The proposed framework can support policymakers in improving the accuracy, transparency, and consistency of poverty intervention strategies.