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Several sub-districts are cities that are pretty or even very vulnerable to flood disasters. Therefore, the Government must observe which areas are highly prone to flooding to anticipate safety precautions. One of the relevant methods for handling this case is k-means clustering using the Silhouette Coefficient and Davies-Bouldin Index evaluation. This research uses a quantitative approach using the Silhouette Coefficient and Davies Bouldin Index Analysis method. Based on the research that has been carried out, the results can be obtained that cluster 1 consists of the sub-districts of Medan Polonia, Medan Johor, Medan Selayang, Medan Baru, Medan Tuntungan and Medan Barat. Cluster 2 only has one member, namely the Medan Maimun sub-district; Cluster 3 consists of Medan Sunggal and Medan Belawan sub-districts, and Cluster 4 consists of Medan Marelan, Medan Helvetia and Medan Timur sub-districts. The results of the clustering evaluation that was carried out obtained a Silhouette Coefficient value of 0.505539942 and a Davies-Bouldin Index value of 0.30055. This means that the grouping carried out in this research is accurate.

This thesis aims to apply model-based clustering in grouping regencies/cities in North Sumatra based on Community Welfare Indicators to determine the number of groups (clusters) formed based on community welfare indicators in regencies/cities in North Sumatra and to understand the level of community welfare from this grouping in planning and managing community welfare in regencies/cities in North Sumatra, with the hope of achieving equal welfare in every region. The research method used is Model-Based Clustering, which uses 5 research data variables: HDI, Poor Population, Unemployment Rate, GDP, and Health. In this millennial era, the assessment of community welfare requires more attention. Rapid social, technological, and environmental changes have created new dynamics that can affect community welfare. The evaluation of community welfare is not only limited to economic parameters but also considers health, unemployment, and other factors. By using Model-Based Clustering, it is possible to determine the optimal number of groups (clusters) from various and possibly correlated variables, and the results are easier to understand, making the analysis and understanding of the results easier. Readers can learn about the level of community welfare, and the community and government can evaluate their welfare for future improvements. The research results show that among the groups of regencies/cities formed, five cities consistently show lower Gross Regional Domestic Product (GDP) and Human Development Index (HDI) than other cities. Therefore, a sustainable approach is needed to improve these cities' economic conditions and social welfare.