A Smart Management Framework for Higher Education Accreditation Based on the PUDAL Engine and V-Model Approach

Daniel Jahja Surjawan, Armein Z.R. Langi, Jaka Sembiring, Albarda, Rosilah Hassan,
dx.doi.org/10.17576/apjitm-2026-1501-10

Abstract

Higher Education Institutions (HEIs) worldwide face growing pressure to reconcile traditional academic values with rapid digital transformation and increasingly stringent accreditation requirements. This paper proposes a novel Smart Management Framework (SMF), grounded in the smart engineering paradigm, to enhance quality assurance and optimize the PPEPP (Planning, Implementation, Evaluation, Control, and Improvement) cycle for accreditation compliance. Central to the framework is the PUDAL (Perception, Understanding, Decision, Action, Learning) engine, which functions as a cognitive intelligence layer that enables context awareness, adaptive decision-making, and autonomous learning from accreditation-related data. The framework is developed using a holistic methodology that integrates a four-layer ASTF (Application, System, Technology, Fundamental) architecture with a V-Model engineering process, providing a structured and data-driven approach to strategic management. Initial results demonstrate the feasibility of automated data collection and restructuring from heterogeneous institutional sources, establishing a critical foundation for intelligent quality assurance processes. This research contributes a systematic, technology-based solution to address data fragmentation and administrative burden in HEIs, while offering a proactive mechanism for continuous quality improvement. The key novelty of the proposed framework lies in the seamless integration of the PUDAL engine with the PPEPP cycle, representing a paradigm shift from conventional reactive quality assurance models toward an smart, adaptive, and learning-oriented system.

keyword

accreditation, PPEPP cycle, PUDAL engine, V-model

Area

Information Systems