Analysis of CSR Program Against Regional Inequality in Bogor Regency Using K-Means and Random Forest Algorithms

Abstract

Bogor Regency is vast and has significant economic and social potential. Collaboration between businesses and local governments is essential to achieve regional development goals. Corporate Social Responsibility (CSR) plays a role in sustainable economic, enhancing the quality of life for the community. CSR can be implemented independently by companies or supported by the CSR Support Group (TF-TJSL). The Bogor Regency is divided into three development areas, the Western, Central and Eastern, with regional inequality reflected in a Williamson index of 0.731. CSR has the potential to reduce these inequalities through positive contributions. This study analyzes CSR programs on regional inequality in Bogor Regency using data mining technology with K-Means and Random Forest algorithms. The K-Means algorithm shows the optimal result with the best silhouette score at K=2 with a score of 0.76268, reflecting a clear separation between clusters representing regional inequality. The Random Forest algorithm shows excellent classification ability with an accuracy of 0.985 and other evaluations of precision, recall, and f1-score are almost perfect, which indicates its effectiveness in classifying data into three clusters according to development areas. The regression model evaluation results are also good, with a very low MSE (0.003961), indicating minimal prediction error.