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All Journal Jurnal Teknik Informatika C.I.T. Medicom
Hengki Tamando Sihotang
Sains Data, Universitas Pembangunan Nasional Veteran Jakarta
Computer Science & IT

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Decision Support System for Determining Cyber Risk Mitigation Priorities in Higher Education Using the Fuzzy TOPSIS Method Fristi Riandari; Hengki Tamando Sihotang
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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The increasing frequency and sophistication of cyber threats have made higher education institutions attractive targets for cyberattacks, posing significant risks to information assets, academic operations, and institutional reputation. Universities rely heavily on digital technologies, including academic information systems, e-learning platforms, cloud services, and research databases, making effective cybersecurity risk management essential. However, limited cybersecurity resources often prevent institutions from addressing all potential threats simultaneously, highlighting the need for a systematic approach to prioritizing cyber risk mitigation efforts. This study aims to develop a Decision Support System (DSS) for determining cyber risk mitigation priorities in higher education institutions using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method. Six evaluation criteria were considered, namely probability of occurrence, financial impact, operational impact, reputation damage, data sensitivity, and recovery complexity. Expert assessments were expressed using linguistic variables and converted into Triangular Fuzzy Numbers (TFNs) to accommodate uncertainty in the decision-making process. The Fuzzy TOPSIS method was then applied to evaluate and rank cyber risks according to their mitigation priorities. The results demonstrated that the proposed DSS successfully generated a prioritized ranking of cyber risks, with ransomware and data breach risks receiving the highest mitigation priorities due to their substantial impacts on university operations, financial resources, and information security. The findings suggest that the developed DSS effectively supports cybersecurity decision-making by handling uncertainty in expert assessments and providing systematic recommendations for cyber risk mitigation. Consequently, the proposed framework can assist higher education institutions in allocating cybersecurity resources more efficiently and enhancing their overall cybersecurity resilience.
Machine Learning Integration in DEA Models: Current Developments and Future Challenges Hengki Tamando Sihotang; Fristi Riandari; Rasenda Rasenda; Wildan Alrasyid
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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The increasing availability of large and complex datasets has created new opportunities for enhancing Data Envelopment Analysis (DEA) through the integration of Machine Learning (ML) techniques. This study reviews current developments in the integration of ML and DEA models and identifies key challenges, trends, and future research opportunities. A systematic literature review was conducted by examining recent studies that combine DEA with various machine learning algorithms across multiple application domains, including healthcare, banking and finance, manufacturing, supply chain management, energy, agriculture, and higher education. The findings indicate that Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, Gradient Boosting methods, and Deep Learning models are among the most frequently employed techniques in DEA-ML frameworks. Despite these advantages, several challenges remain, including data quality issues, model interpretability, computational complexity, limited generalizability, and the lack of standardized integration frameworks. The review concludes that the integration of ML and DEA offers substantial potential for advancing efficiency analysis and organizational performance evaluation. Future research should focus on developing explainable artificial intelligence (XAI) solutions, real-time efficiency analytics, federated learning approaches, and standardized hybrid DEA-ML frameworks to improve transparency, scalability, and practical applicability across diverse operational environments.
Co-Authors Fristi Riandari Fristi Riandari Rasenda Rasenda Wildan Alrasyid