Automating Performance-Based Budgeting Using a Knowledge-Based System

This study investigates the implementation of a Knowledge-Based System (KBS) integrated with the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to automate performance-based budgeting in a public university environment. The integration of Fuzzy AHP enhances the system’s ability to manage uncertainty and subjectivity in expert assessments, resulting in more consistent prioritization of performance indicators and improved decision accuracy. Data was obtained through interviews, questionnaires, and field observations, supported by institutional financial and performance reports. The developed system architecture—comprising a knowledge base, inference engine, and user interface—enables structured, transparent, and knowledge-driven budgeting analysis. The findings show that the system strengthens objectivity, coherence, and strategic alignment in the budgeting process while promoting accountability and efficiency in financial management. For university finance managers and administrators, this system provides a practical decision-support tool that facilitates data-based resource allocation and enhances institutional performance monitoring. The novelty of this research lies in the combination of Fuzzy AHP and KBS methodologies, offering an innovative model for intelligent, performance-oriented financial management in higher education institutions.