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Pengembangan Sistem Informasi Desa Berbasis Web untuk Monitoring Kependudukan dan Pertanian Desa Jaya Santoso; Herimanto Herimanto; Ranty Deviana Siahaan; Arlinta Christy Barus; Arie Satia Dharma; Johannes Harungguan Sianipar
Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : FKIP Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36709/amalilmiah.v7i2.538

Abstract

Pengembangan sistem informasi desa berbasis web menjadi kebutuhan penting dalam mendukung tata kelola data di tingkat desa, khususnya terkait kependudukan dan pertanian. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Desa Kuta Dame, Kabupaten Pakpak Bharat, dengan tujuan merancang dan mengimplementasikan sistem informasi desa yang mampu memfasilitasi monitoring  kependudukan, penguasaan lahan, dan aktivitas pertanian. Metode kegiatan meliputi analisis kebutuhan mitra untuk mengetahui permasalahan utama, perancangan sistem menggunakan pendekatan terstruktur, implementasi berbasis teknologi web dengan desain antarmuka yang sederhana dan mudah digunakan, serta diseminasi hasil melalui pelatihan dan pendampingan aparatur desa. Hasil kegiatan menunjukkan bahwa sistem berhasil mengintegrasikan 2.563 data penduduk ke dalam platform digital serta digunakan oleh 40 aparatur desa dalam kegiatan pelatihan dan uji coba operasional. Sistem mampu menyajikan informasi kependudukan dan pertanian secara sistematis, terintegrasi, dan dapat diakses secara real-time, sehingga meningkatkan efisiensi pengelolaan data serta mempermudah proses monitoring  dan pengambilan keputusan di tingkat desa. Dengan demikian, pengembangan sistem informasi desa berbasis web terbukti mendukung peningkatan kapasitas digital aparatur desa dan memperkuat tata kelola pemerintahan desa yang lebih efektif dan transparan.
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.