<![CDATA[Population growth clustering is a crucial process for analyzing and understanding population patterns. This paper presents research aimed at investigating and analyzing population growth clustering methods in a specific context. The study employs techniques and algorithms such as cluster analysis, hierarchical clustering, and growth models. The research methodology includes the collection of relevant demographic, social, economic, geographical, and cultural data. This data is analyzed using appropriate statistical methods to identify significant patterns and population structures. The findings offer valuable insights into the distribution and structure of the population, focusing on the application of the K-Means algorithm for clustering population growth based on sub-districts in Kebumen Regency. These findings can support development planning, social policies, and resource management. This study significantly contributes to the understanding and practice of population growth clustering, providing an important reference for researchers, practitioners, and policymakers interested in clustering and analyzing populations across various geographical and social contexts. Keywords:Population Clustering, Cluster Analysis, Hierarchical Clustering, Model-Based Clustering, Population Patterns.]]>
Copyrights © 2024
