Analisis Perbandingan Algoritma K-MEANS dan DBSCAN Clustering Kondisi Ekonomi Masyarakat di Kelurahan Pulo Brayan Darat 1

Considering the current data-driven policies, economic disparities across metropolitan areas are an important factor to consider. This study focuses on a comparative analysis between the K-MEANS and DBSCAN algorithms in the process of clustering residents in Pulo Brayan Darat 1 Village, taking into account the economic conditions of each individual. Through this study, it is hoped that the more optimal algorithm for categorizing the community based on their economic well-being can be identified. A survey was used to collect data, including questions about income, number of children, and type of employment. The timing of implementation, interpretation of clustering findings, and the Silhouette Index were all used in the evaluation process. It is clear from the findings that DBSCAN can identify outliers and adapt better to irregular data patterns, while K-MEANS is faster and creates more organized clusters. This research offers lessons for policymakers in developing focused community welfare development programmers, which also aids in selecting an appropriate clustering system for socio-economic data.