Transformasi Sistem Informasi Manajemen Pendidikan Berbasis AI untuk Akurasi Pengambilan Keputusan Akademik

Abstract

The rapid advancement of Artificial Intelligence (AI) has brought significant changes to the management of educational institutions. This study aims to analyze the transformation of Artificial Intelligence-based Education Management Information Systems (EMIS) in improving the accuracy of academic decision-making. This research employed a descriptive qualitative approach through a literature review and analysis of AI implementation in educational management systems across several higher education institutions. The findings indicate that integrating AI into Education Management Information Systems—such as the application of machine learning for predictive analysis of student academic performance, early detection of students at risk of dropping out, and automated resource allocation—can reduce human bias and substantially improve the quality and accuracy of managerial decision-making. Nevertheless, several challenges remain, including the readiness of data infrastructure, the protection of student data privacy, and the need to enhance the digital literacy of academic and administrative staff. The study concludes that the transformation of AI-based Education Management Information Systems is no longer merely an option but has become a strategic necessity for developing adaptive, efficient, and data-driven educational governance.