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A Spatial Analysis-Based PSI Model for Post-Crisis Social Vulnerability Mitigation Priorities Muit Sunjaya; Akbar Idaman; Hamidah Azzahra S. Lubis
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32032

Abstract

Post-crisis social vulnerability is a crucial factor influencing a region's ability to face, respond to, and recover from a disaster. Differences in socioeconomic conditions, access to basic services, demographic characteristics, and adaptive capacity cause variations in the level of social vulnerability between regions. This study aims to develop a social vulnerability mitigation priority model in Medan City by integrating the Preference Selection Index (PSI) method with a sub-district-based spatial analysis approach. The study used 21 sub-districts in Medan City as decision alternatives evaluated based on seven criteria: economic vulnerability, disruption of access to basic services, post-crisis social impacts, demographic vulnerability, community social capital, community recovery capacity, and institutional and mitigation support. Research data were obtained through questionnaires and then aggregated into a decision matrix for the PSI calculation process. The PSI method was used to objectively generate criteria weights and obtain preference index values for each region. Next, the preference index values were transformed into a mitigation priority index using the inverse approach PMi=1−θi, where a higher PM value indicates a greater need for mitigation interventions. The results of the study indicate that Medan Belawan District has the highest mitigation priority with a PSI value of 0.5556 and a PM value of 0.4444, followed by Medan Labuhan District with a PSI value of 0.5986 and a PM value of 0.4014, and Medan Marelan District with a PSI value of 0.6253 and a PM value of 0.3747. These results indicate that the integration of the PSI method and spatial analysis is able to produce a data-based decision support model to identify priority areas for mitigating social vulnerability more objectively and systematically.