Development of an Intelligent Clergy Management and Matching Platform for Optimized Church-Priest Deployment

Abstract

This study presents the development of an intelligent clergy management and matching platform designed to optimize church–priest deployment and improve administrative decision-making. The system leverages machine learning algorithms, specifically Decision Tree and Random Forest, to automate ministerial recommendation and workers’ disposition based on data-driven insights. The model is trained using integrated datasets, including church profiles, financial records, workers’ disposition history, and ministerial profiles within the Anglican communion. By aligning the strengths and experience of clergy with the needs of individual church stations, the system enhances the accuracy and efficiency of deployment decisions.