Modeling Multidimensional Social Welfare Using Machine Learning Analysis
DOI:
https://doi.org/10.31849/digitalzone.v17i1.32730Keywords:
Sosial welfare, multidimensional analysis, machine learning, benefit–cost, K-MeansAbstract
The measurement of regional welfare in Indonesia is still dominated by economic indicators and therefore does not fully capture broader social conditions. This study aims to model and analyze multidimensional social welfare across provinces in Indonesia using a machine learning approach. The analysis is based on official data from Statistics Indonesia (BPS), covering five key dimensions: health, economy, education, infrastructure, and employment. The data were transformed using Benefit–Cost normalization and analyzed using the K-Means clustering algorithm. The results reveal four distinct welfare clusters with varying characteristics. The highest welfare cluster achieves an average index value of 0.6143, while the medium and lower-middle clusters record values of 0.4673 and 0.4210, respectively. The lowest welfare cluster has an index value of 0.3468, indicating limitations in health, economic, and employment dimensions. These findings highlight significant disparities in regional welfare conditions and demonstrate the importance of a multidimensional analytical approach. This study contributes by integrating multidimensional welfare indicators with a machine learning clustering method to provide a more objective and data-driven classification of regional welfare.
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