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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Edumatsains BAREKENG: Jurnal Ilmu Matematika dan Terapan Compiler Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Pengabdian Masyarakat Mandira Cendikia (JPKM-MC) KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Jaya Santoso
Institut Teknologi Del
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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A Composite Centrality Framework for Evacuation Planning in Meso-Scale Spatial Networks with Semi-Structured Connectivity Jaya Santoso; Ana Muliyana; Asido Saragih; Ridho Pakpahan; Debora Chrisinta
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15916

Abstract

Evacuation planning in spatial networks requires the identification of critical nodes that maintain connectivity, accessibility, and flow distribution during emergency situations. Existing approaches often rely on individual centrality measures, which capture only a single structural dimension of node importance and may therefore produce incomplete or biased prioritization. To address this limitation, this study proposes a Composite Centrality Framework for identifying critical nodes in meso-scale spatial networks with semi-structured connectivity. The network is modeled as a weighted undirected graph, and Degree, Betweenness, and Closeness Centrality are integrated into a unified composite index to capture complementary structural roles. The framework is implemented in MATLAB and evaluated using a real-world campus spatial network consisting of 30 nodes and a synthetic network comprising 16 nodes with comparable structural characteristics. The results reveal a highly uneven distribution of node importance, with a small set of structurally dominant nodes consistently identified across both networks. In the campus network, node P1 achieves the highest composite centrality score (0.2195) and ranks first across the individual centrality measures, indicating its dominant role in maintaining network connectivity, accessibility, and flow distribution. Quantitative evaluation demonstrates strong agreement between the composite ranking and the individual measures, with Spearman rank correlation coefficients of 0.94, 0.89, and 0.91 for Degree, Betweenness, and Closeness Centrality, respectively. However, only one node (P1) appears simultaneously in the top five of all rankings, highlighting the complementary nature of the individual centrality measures and supporting the need for multi-criteria integration. Sensitivity analysis across three weighting scenarios yields rank correlations exceeding 0.97, confirming ranking stability and methodological robustness. Overall, the proposed framework provides a balanced and reliable approach for identifying critical nodes and demonstrates potential applicability to evacuation planning and spatial network analysis in semi-structured environments.
Co-Authors Ana Muliyana Andrew Rolas Siagian Arie Satia Dharma Arlinta Christy Barus Asido Saragih Chrisinta, Debora Darmaji, Darmaji Debora Chrisinta Febri Sihotang Herimanto Herimanto Johannes Harungguan Sianipar JUNITA AMALIA Martgrita, Merry Meryam Miko Purnomo Ragina Ayunita Tarigan Ranty Deviana Siahaan REGINA AYUNITA TARIGAN Ridho Pakpahan Sari Muthia Sari Sari Muthia Silalahi Simarmata, Justin Eduardo