Pendekatan Data-Driven untuk Pengambilan Keputusan pada Institusi Perguruan Tinggi

Abstract

Data-driven approaches, which leverage data for various purposes, are increasingly adopted by organizations, including higher education institutions. These approaches focus on thorough data analysis, interpretation, transformation, and presentation to inform organizational decision-making. However, higher education institutions often face obstacles such as outdated technology infrastructures, rigid governance structures, and susceptibility to ongoing regulatory changes, which can impede the support needed for managerial decision making. This research addresses the gap by proposing a data-driven methodology to enhance decision-making processes within the academic domain of higher education institutions, utilizing various data preprocessing techniques and machine learning algorithms to integrate, to enhance data quality and to analyze data from multiple systems within a university environment. Three clustering models were developed: Model A (demographic attributes), Model B (academic performance attributes), and Model C (a combination of both). Results indicate that socio-demographic attributes negatively impact clustering cohesion, with Model B achieving the highest performance with a silhouette score of 0.56. The findings demonstrate the potential of a data-driven approach to provide comprehensive information that aids university leaders in policy-making, ultimately improving institutional quality and competitiveness.