Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Vita Ratnasari*, Andrea Tri Rian Dani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Problems related to food are indeed an issue that continues to be discussed by the government, both imports, self-sufficiency, the issue food security. Food security conditions have become one of the biggest problems in Indonesia, even though Indonesia is an agricultural country with abundant resources. The problem is not only the availability but also the affordability. It happens due to the social inequality between the rich and the poor, which means the rich can easily relish food. People with low incomes experience food insecurities. Thus, an appropriate strategy and policies can be done for each province in Indonesia to make it equal. Cluster analysis is used to map the provincial profile based on the condition of food security. However, the variable types in this research are numerical and categorical data, which makes general cluster analysis insufficient. This study used the Cluster Ensemble Based Mixed Data Clustering-Robust Clustering Using Links (CEBMDC-ROCK) method to cluster provinces in Indonesia based on food security conditions. The analysis process starts with numerical clustering data using Agglomerative Hierarchical Clustering (AHC) and then with categorical data using Robust Clustering Using Links (ROCK). The result shows that the province in Indonesia is divided into five groups based on the quality of food security, which is from very low to excellent. Based on the clustering results, which provinces need special attention from the government regarding food security can be seen.

Original languageEnglish
Pages (from-to)611-617
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume13
Issue number2
DOIs
Publication statusPublished - 2023

Keywords

  • Cluster analysis
  • food security
  • mixed data
  • robust clustering

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