Development of a Smart Quality Assurance Framework Based on Forensic Audit 5.0 with Artificial Intelligence Support to Enhance Internal Quality Assurance Systems in Higher Education: Alignment with Permendiktisaintek No. 39 of 2025 - Ali Mutaufiq; Mizwar Anwar; Diana Purnamasari; Vonny Novera; Eddy Purnairawan; Desti Kumala (Doctoral Program of Social Science Pasundan University, 2026)
The promulgation of the Ministry of Higher Education, Science, and Technology Regulation (Permendiktisaintek) No. 39 of 2025 institutes a systemic transformation in the governance and quality assurance paradigms of Indonesian Higher Education Institutions (HEIs). By simplifying accreditation tiers into a streamlined categorical system, decentralizing curriculum design to accommodate flexible pathways such as the Recognition of Prior Learning (RPL), and mandating absolute data integration with the national Higher Education Database (PD Dikti), the regulatory framework demands an unprecedented level of institutional agility, accountability, and data veracity. However, empirical observations indicate that traditional Internal Quality Assurance Systems (SPMI) are structurally constrained by analog workflows, fragmented data architectures, and retroactive evaluation methodologies that fail to pre-emptively mitigate compliance deviations. This comprehensive research report proposes the conceptualization and implementation of a Smart Quality Assurance (SQA) Framework, fundamentally anchored in the principles of Forensic Audit 5.0 and augmented by Artificial Intelligence (AI). The proposed framework integrates real-time continuous auditing mechanisms, machine learning-driven anomaly detection utilizing Classification and Regression Tree (CART) algorithms, and blockchain-based immutable ledgers to transform the standard Penetapan, Pelaksanaan, Evaluasi, Pengendalian, dan Peningkatan (PPEPP) cycle into a proactive, predictive, and strategically aligned digital ecosystem. By leveraging real-time data analytics and Explainable AI (XAI) such as SHapley Additive exPlanations (SHAP), the SQA framework addresses inherent vulnerabilities in academic governance, demonstrating empirical potential to reduce processing latency by up to 73% while enhancing data accuracy by 89%. The findings conclusively suggest that adopting AI-driven forensic auditing not only guarantees rigid compliance with the National Standards for Higher Education (SN Dikti) but also provides a scalable, ethically governed pathway for institutions to achieve the coveted