COLLABORATION AMONG GOVERNMENT LEVELS IN ADDRESSING SETTLEMENT AREA PROBLEMS IN SUMBAWA REGENCY THROUGH A CLUSTERING STRATEGY
Keywords:
Cluster Analysis, Slum Areas, Uninhabitable Houses, Housing Backlog, K-Means ClusteringAbstract
This study analyzes the use of the K-Means method to cluster villages/urban areas in Sumbawa Regency based on the similarity of characteristics such as population size, slum area, and housing data in order to provide a clear strategy for addressing issues at different levels of government: central, provincial, and local. The data collection method employed is secondary data obtained from the Housing and Settlement Area Office of Sumbawa Regency for the year 2023. The data used includes the following variables: Slum Area (X1), Population Size (X2), Number of Houses (X3), Number of Uninhabitable Houses (RTLH) (X4), and Housing Backlog (X5). The data was analyzed using SPSS software. Based on regulatory considerations, the clustering is proposed to consist of three clusters. According to the K-Means method, Cluster 1 consists of 1 village, Cluster 2 consists of 13 villages/urban areas, and Cluster 3 consists of 45 villages/urban areas. Each cluster is then grouped based on the similarity of their characteristics. Specifically, Cluster 1 is categorized as high in terms of average values for variables X1, X2, X3, and X4 (above the population average). Cluster 2 is categorized as medium, with average values for X1, X2, X3, and X4 (at the population average). Cluster 3 is categorized as low, with below-average values for all variables (below the overall population average).
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