Stacking-Correspondence Analysis for Fuzzy Data: Computational Framework for Analyzing Complex Qualitative Survey Data - Disa Rahma Kirana; Irlandia Ginanjar; Bertho Tantular (Forum Kerjasama Pendidikan Tinggi, 2026)
Bandung Regency faces a significant challenge in achieving Sustainable Development Goal (SDG) 12, marked by a critically low score of 14.53 out of 100. Uniform policies are often ineffective due to regional diversity and uncertainty in categorical survey data, which inadequately reflects real-world conditions. This study aims to identify sub-district characteristics based on consumption and production patterns to provide precise policy recommendations. The research utilizes data from the 2024 Supporting Area Survey (SWP), covering 280 villages across 31 sub-districts. A computational framework combining stacking techniques and Correspondence Analysis for Fuzzy Data (CAFD) is implemented to analyze four qualitative variables. The stacking phase transforms the multi-way data structure into a two-way structure, while CAFD effectively handles qualitative uncertainty using membership degrees. Analysis results indicate that two principal dimensions capture 73.35% of the total information variance and successfully identify 17 sub-district clusters with similar problem profiles. The fuzzy approach unveils multi-characteristic profiles, identifying both dominant and secondary traits. This research contributes a two-dimensional perceptual map, enabling the government to transition from generic policies to tailored interventions for each sub-district. This computational solution represents a concrete step toward improving the SDG 12 achievement score through data-driven strategic planning.