Disa Rahma Kirana, Irlandia Ginanjar, Bertho Tantular

Stacking-Correspondence Analysis for Fuzzy Data: Computational Framework for Analyzing Complex Qualitative Survey Data

Introduction

Stacking-correspondence analysis for fuzzy data: computational framework for analyzing complex qualitative survey data. Analyze complex qualitative survey data for SDG 12 in Bandung Regency. This computational framework, using fuzzy data analysis, identifies sub-district clusters for tailored policy recommendations.

Abstract

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.

Review

This study addresses a critically important challenge: the achievement of Sustainable Development Goal 12 in Bandung Regency, where current indicators reveal a significant deficit. The authors clearly articulate the limitations of uniform policies and traditional survey data, particularly regarding the inherent uncertainty in qualitative assessments that often fail to capture the nuanced realities of diverse sub-districts. The paper's aim to identify specific consumption and production patterns for tailored policy recommendations is highly relevant and timely, promising a tangible pathway to improve regional planning and governance effectiveness, moving beyond generic solutions to data-driven interventions. Methodologically, this research proposes an innovative computational framework that commendably tackles the complexities of qualitative survey data. The integration of stacking techniques to transform multi-way data into a manageable two-way structure, combined with Correspondence Analysis for Fuzzy Data (CAFD), represents a sophisticated approach to data analysis. The use of CAFD is particularly noteworthy, as it effectively handles the crucial aspect of uncertainty and vagueness in qualitative variables through membership degrees, a significant advancement over methods that might simplify or discard such vital information. The application to a comprehensive dataset covering 280 villages across 31 sub-districts further underscores the robustness and practical applicability of the proposed framework. The results presented are compelling and offer direct utility for policymakers. The identification of two principal dimensions capturing a substantial 73.35% of information variance, alongside the successful clustering of 17 sub-districts with similar problem profiles, provides a clear basis for differentiated policy. Crucially, the fuzzy approach's ability to reveal multi-characteristic profiles, distinguishing between dominant and secondary traits, offers a richer, more nuanced understanding of each sub-district's situation. The resulting two-dimensional perceptual map is a valuable output, translating complex analytical findings into an accessible tool that directly supports the transition from generic to tailored interventions, representing a concrete and impactful step toward improving SDG 12 achievement through strategic, data-driven planning.

Full Text

Comments

You need to be logged in to post a comment.