Predictive Insights into Talent Management: A Random Forest Approach to Assessing Top Talent in State-Owned Enterprises

This study examines how talent potential in State Owned Enterprises (Badan Usaha Milik Negara/BUMN) can be evaluated using cloud-based machine learning tools, notably Amazon Web Services (AWS). The study emphasizes the need to recognize employees who have the potential to become future leaders or decision-makers within BUMN. This project aims to assess talent potential using parameters specified in BUMN Ministerial Regulations and to show the viability of doing talent analysis in a cloud environment, particularly AWS, to provide improved flexibility and integration. The study's data is gathered via the AWS cloud platform, allowing users, known as Talent Analysts, to conveniently upload data in CSV format. The data then goes through a thorough process that includes training, modeling, and testing, all carried out using AWS tools and the Python programming language. The Random Forest Algorithm is utilized as a crucial element in the analysis for machine learning. The research yielded good results, with a test data accuracy percentage of 92.1%. Additionally, the results incorporate a visualization feature that helps pinpoint top talent and nominated talent in organizations. This study enhances talent management techniques and highlights the capabilities of cloud-based platforms, like AWS, for undertaking versatile and comprehensive talent evaluations.)