A Fuzzy Inference System Sugeno Based Framework for Evaluating the Quality of Islamic Education Institutions

Abstract

This study aims to develop a conceptual framework for evaluating the quality of Islamic educational institutions using a Sugeno based Fuzzy Inference System (FIS). Islamic education encompasses multiple dimensions spiritual, academic, managerial, and social that are inherently complex and often subjective, making conventional evaluation methods insufficient. The study employs a qualitative library research approach, analyzing peer-reviewed journals, academic books, and institutional quality reports to construct a comprehensive FIS Sugeno model. Key quality indicators, including teacher competence, curriculum relevance, facilities, management, and graduate outcomes, are defined as fuzzy input variables and processed through fuzzification, rule-based inference, and defuzzification to generate objective and consistent quality scores. The conceptual model demonstrates that the Sugeno FIS effectively manages uncertainty and ambiguity in qualitative data, providing a systematic and transparent evaluation framework. The proposed approach not only enhances the accuracy and reliability of quality assessments but also supports educational institutions in formulating adaptive and context-sensitive strategies for continuous improvement. The study contributes both theoretically and practically by integrating artificial intelligence methods into Islamic education management and offering a foundation for further empirical applications across madrasahs, pesantrens, and higher education institutions.