Revisiting social vulnerability analysis in Indonesia: An optimized spatial fuzzy clustering approach

B.I. Nasution, R. Kurniawan, T.H. Siagian, A. Fudholi

Research output: Contribution to journalArticlepeer-review


In previous social vulnerability studies, the use of indices in assessing social vulnerability can only describe conditions of social vulnerability in general without being able to indicate which factors are dominant in measuring the level of social vulnerability. Therefore this study aims to fill the gap by implementing the relative approach using the clustering method to characterize the dominant factors of social vulnerability in Indonesia at the district level utilizing hybridization of the Fuzzy Geographically Weighted Clustering (FGWC) with Intelligent Firefly Algorithm (IFA). Then the Kruskal-Wallis test is used to obtain the dominant factor of social vulnerability in each formed cluster. The study found that each district in Indonesia has its own prevailing social vulnerability problems. The majority of regions on the west coast of Sumatera Island, such as Nias and Mentawai District and Eastern Indonesia, such as Sumba Barat Daya and Intan Jaya District, are the districts with the most problem, particularly in socioeconomic aspects. The results of this study can be used to support the prevention, mitigation, response, and recovery for reduction disaster programs in Indonesia.
Original languageEnglish
Article number101801
Pages (from-to)1-11
Number of pages11
JournalInternational Journal of Disaster Risk Reduction
Early online date28 Aug 2020
Publication statusPublished - 1 Dec 2020


  • social vulnerability
  • fuzzy geographically weighted clustering
  • intelligent firefly algorithm


Dive into the research topics of 'Revisiting social vulnerability analysis in Indonesia: An optimized spatial fuzzy clustering approach'. Together they form a unique fingerprint.

Cite this