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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat Dharma Bakti International Journal of Advances in Data and Information Systems Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Abdi Masyarakat Indonesia Jurnal INFOTEL
Windyaning Ustyannie
Institut Sains & Teknologi AKPRIND
Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Geospatial Model Optimization for Mapping Social Vulnerability to Natural Disasters Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm Istiawan, Deden; Wulandari, Ratri; Ustyannie, Windyaning
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1452

Abstract

This study analyzes social vulnerability to natural disasters in Indonesia through a geospatial optimization model integrating Fuzzy Geographically Weighted Clustering (FGWC) with the Flower Pollination Algorithm (FPA). The hybrid FGWC–FPA enhances clustering accuracy by optimizing spatial parameters and addressing the limitations of index-based and non-spatial methods. The model tested two to four clusters, with the optimal configuration producing four distinct vulnerability groups. Cluster 1 (114 districts) exhibits high poverty, weak infrastructure, and low literacy; Cluster 2 (79 districts) reflects demographic pressure and gender-related inequality; Cluster 3 (87 districts) shows low education and poor disaster preparedness; while Cluster 4 (234 districts) represents health- and age-related vulnerability. A comparison with the 2024 Indonesian Disaster Risk Index (IRBI) shows strong spatial consistency, especially in high-risk regions such as Papua, Maluku, and Sulawesi. The FGWC–FPA model provides finer spatial granularity, allowing the identification of region-specific social issues not captured by deterministic index approaches. The findings validate national disaster risk patterns and offer complementary insights for implementing the National Disaster Management Master Plan (RIPB) 2020–2044, supporting regional prioritization, resource allocation, and capacity-building strategies.
Optimization-Based Geospatial Clustering Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm for Stunting Risk Mapping Ngatimin, Ngatimin; Istiawan, Deden; Ustyannie, Windyaning; Riansyah, Rahmat; Sholicah, Ameliatus
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3130.151-164

Abstract

Stunting remains a major public health challenge in Indonesia, characterized by significant regional disparities and complex multidimensional determinants. Effective intervention strategies therefore require analytical approaches that are capable of capturing spatial heterogeneity and identifying region-specific vulnerability patterns. This study applies Fuzzy Geographically Weighted Clustering (FGWC) optimized using the Flower Pollination Algorithm (FPA) to map district-level stunting vulnerability and identify priority intervention areas. The analysis covers 514 districts using 21 multidimensional indicators representing health, nutrition, housing conditions, food security, social protection, and demographic characteristics derived from the Central Statistics Agency. The integration of FGWC with FPA enhances clustering performance by incorporating spatial dependence and metaheuristic optimization, enabling the algorithm to produce more stable and geographically sensitive clusters. Cluster validity indices confirm that a four-cluster solution provides the most optimal segmentation of stunting vulnerability. The results reveal distinct regional structures, socioeconomic-driven vulnerability associated with limited asset ownership, high dependence on social assistance and large household size, multidimensional deprivation concentrated primarily in eastern Indonesia, and nutrition-related vulnerability linked to breastfeeding duration and food security. These findings demonstrate that stunting patterns in Indonesia are spatially heterogeneous and influenced by diverse structural factors. The proposed FGWC–FPA framework offers a robust geospatial optimization approach that can support more precise, evidence-based, and region-specific strategies for accelerating stunting reduction.
Co-Authors Agus Hindarto Wibowo Agus Hindarto Wibowo Almuntaha, Eska Argaditia Mawadati Bagas Robi Arjianto Catur Iswahyudi Dwi Setyowati Eka Sulistyaningsih Emy Setyaningsih Endang Widuri Asih Endang Widuri Asih Erma Susanti Harmastuti Harmastuti Harmastuti Istiawan, Deden Khasanah, Rahayu NGATIMIN, NGATIMIN Ratri Wulandari Riansyah, Rahmat Sambuari , Meychel Danius Fedrix Santoso, Arif Gunawan Sholicah, Ameliatus Sulistijanti, Wellie Sulistyaningsih, Eka Suprapto Suprapto Widuri Asih, Endang Zaitunnah, Anis