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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 152 Documents
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Expert System for Identification of Digital Transformation Maturity Level in Secondary Schools Using Forward Chaining Rizki Nur Afifah
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid growth of digital transformation in education has significantly influenced secondary schools through the adoption of e-learning platforms, digital administration systems, and smart classroom technologies, making digital maturity an essential aspect of modern educational development. However, many schools still lack a structured and standardized approach to measure their level of digital transformation maturity, resulting in assessments that are often subjective and inconsistent. This study aims to develop an expert system for identifying the digital transformation maturity level of secondary schools. The system is built using a rule-based approach supported by a knowledge base derived from expert interviews and relevant literature, including established digital maturity frameworks. Data collection is conducted through structured questionnaires that capture key indicators such as ICT infrastructure, teacher digital literacy, digital learning adoption, and institutional policy support. The inference mechanism employed in the system is Forward Chaining, which processes input facts and applies IF–THEN rules to generate logical conclusions. The system is capable of classifying schools into predefined maturity levels, ranging from initial to advanced stages of digital transformation. The results indicate that the expert system can effectively evaluate and categorize digital maturity levels in a systematic and consistent manner. In conclusion, the proposed system provides a reliable decision-support tool that assists school administrators and policymakers in assessing and improving digital transformation readiness in secondary education institutions.
A Foundational Framework for Intelligent Data-Driven Decision Support Systems Based on Adaptive Preference Learning Jonhariono Sihotang; Amran Manalu
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 3 (2026): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing complexity of organizational decision-making, driven by heterogeneous data, evolving user preferences, and dynamic business environments, has exposed the limitations of conventional Decision Support Systems (DSS). Traditional DSS rely on static decision models and predefined preferences, limiting their adaptability and personalization. Although Artificial Intelligence (AI)-based DSS have improved predictive capabilities, many still lack adaptive preference learning, continuous feedback, explainability, and lifelong learning. This study aims to develop a Foundational Framework for Intelligent Data-Driven Decision Support Systems (ID-DSS) based on Adaptive Preference Learning (APL). The research adopts the Design Science Research (DSR) methodology, incorporating a systematic literature review, problem identification, requirement analysis, framework design, and conceptual validation. The proposed framework integrates data analytics, adaptive preference learning, decision intelligence, explainable AI, continuous feedback, and knowledge updating within a closed-loop learning architecture. The Adaptive Preference Learning mechanism continuously refines user preferences using explicit feedback, implicit behavioral observations, contextual information, and incremental learning, enabling recommendations to become increasingly personalized and adaptive. Furthermore, explainable AI enhances transparency by providing interpretable reasoning for recommendation outcomes. The proposed framework establishes a theoretical foundation for next-generation intelligent DSS that are adaptive, personalized, transparent, context-aware, and capable of continuous learning, with potential applications across healthcare, finance, manufacturing, education, smart cities, and public administration.

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