Cluster Analysis Using the Ward Algorithm for Grouping Regency / City in Central Java Province Based on Poverty Indicators 2023
DOI:
https://doi.org/10.36456/jstat.vol18.no1.a10160Keywords:
Poverty, hierarchical analysis, Ward method, Central JavaAbstract
Poverty is still a major issue in Central Java Province, with the poverty rate recorded at 10.23% in 2023. Uneven poverty reduction in various regions adds to the complexity of this problem. This study aims to classify districts/cities in Central Java based on poverty indicators, using a hierarchical cluster analysis approach through Ward's algorithm. The data used is secondary data from the Central Bureau of Statistics (BPS), which includes six variables that affect poverty. One them is the percentage poor people (X1), which represents the proportion of the population below the poverty line and is an important indicator in assessing the welfare of a region. The results show that the application of hierarchical cluster analysis through Ward's algorithm produces four clusters of districts/cities based on poverty indicators in 2023, each with different characteristics. The first cluster consists regions with high poverty rates and limitations in education, health, and economic infrastructure. Regions in this cluster require special attention in policy formulation. Based on the characteristics obtained from each cluster, these findings can be used to design more targeted and data-based development policies, such as budget allocation, poverty reduction programs, and improving access to basic services according to the conditions each cluster.
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