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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 5 Documents
 1 
Search results for , issue "Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)" : 5 Documents clear
Exploring Representation-Based Learning Techniques: Toward More Generalized and Self-Optimizing Models Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Representation-based learning has become a foundational pillar of modern machine learning, enabling models to extract meaningful structure from complex, high-dimensional data. This study employs a mixed-method research design that integrates theoretical analysis, systematic literature review, and empirical evaluation to investigate the effectiveness of representation-based learning techniques in developing more generalized and self-optimizing machine learning models. Through an integrated review and empirical evaluation, the research investigates how different representation mechanisms influence model generalization, robustness, and adaptability across diverse data modalities. The findings show that deep, self-supervised, and contrastive representations consistently outperform traditional feature engineering, symbolic approaches, and classical statistical models, particularly in low-data and cross-domain scenarios. However, the study also identifies critical challenges including representation collapse, bias in embeddings, high computational overhead, interpretability limitations, and catastrophic forgetting that must be addressed to realize fully autonomous learning systems. In addition to synthesizing advances such as foundation models, multimodal fusion, neuro-symbolic frameworks, and efficient edge-compatible representations, this research proposes a structured framework for evaluating representation quality and outlines conceptual enhancements for self-optimizing learning systems. Overall, the study offers theoretical insights, practical evaluation tools, and forward-looking perspectives that contribute to the development of more generalized, flexible, and self-improving machine learning models capable of meeting the demands of evolving real-world applications.
Theoretical Advances in Hungarian Maximization Models for Multi-Site Human Resource Allocation Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study presents a theoretical and methodological advancement of the Hungarian maximization model for optimizing multi-site human resource allocation. Traditional Hungarian algorithms focus on single-site, cost-minimization assignments, limiting their applicability in modern workforce environments characterized by distributed operations and diverse employee attributes. To address these gaps, the study reformulates the classical objective function into a maximization framework and incorporates multi-site constraints, multi-criteria employee attributes, and workload balancing requirements. The enhanced model is evaluated through mathematical analysis and simulation-based case studies to assess its performance relative to baseline assignment and heuristic optimization methods. The results demonstrate that the proposed model achieves higher organizational productivity, reduces operational costs, improves staff distribution equity, and significantly accelerates computation time compared with existing approaches. Moreover, the model ensures more consistent alignment between employee capabilities and site-level demands, offering a more robust foundation for strategic workforce deployment. Comparisons with previous studies show that this research provides the first Hungarian-based maximization framework specifically tailored for multi-site HR allocation, overcoming key limitations related to scalability, fairness, and optimality. Overall, this study contributes a rigorous theoretical extension of the Hungarian method and offers practical implications for workforce scheduling, supply-chain staffing, healthcare deployment, and emergency response operations. The findings underscore the potential of deterministic optimization models to support intelligent and equitable human resource decision-making in increasingly complex organizational settings.
A Fundamental Multilevel Optimization Decision Model for Complex Systems Based on an AI-Optimization Fusion Framework Sihotang, Hengki Tamando; Simbolon, Roma Sinta
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1388.pp136-147

Abstract

Complex systems in modern domains such as transportation, energy, supply chains, and autonomous multi-agent networks require decision-making frameworks capable of handling hierarchical structures, dynamic environments, and high levels of uncertainty. Traditional multilevel optimization models offer a structured approach but often struggle with computational complexity, nonlinear interactions, and incomplete information. This research proposes a fundamental multilevel optimization decision model based on an AI-Optimization Fusion Framework designed to overcome these limitations. The model integrates bilevel and trilevel hierarchical structures with artificial intelligence learning paradigms, including supervised learning, deep learning, and reinforcement learning, to form a unified architecture that adapts to evolving system behaviors. A hybrid algorithmic formulation is developed to merge optimization procedures with learning-based approximations, enabling faster convergence, improved robustness, and enhanced decision quality. The experimental and simulation results demonstrate that the proposed framework outperforms traditional optimization approaches in accuracy, computational efficiency, scalability, and resilience under uncertainty. The model’s hierarchical decision mechanisms allow for dynamic coordination across decision levels, while AI-driven components provide predictive and adaptive capabilities that mitigate complexity in high-dimensional environments. The research contributes a novel integrated architecture, theoretical enhancements in multilevel decision modeling, and algorithmic innovations for hybrid AI–optimization systems. Limitations related to data availability, computational resources, and structural assumptions are acknowledged, offering directions for future exploration. Overall, this study establishes a new foundation for intelligent, scalable, and robust decision-making in complex systems, positioning AI–optimization integration as a key enabler for next-generation autonomous and adaptive decision frameworks.
A Dynamic Decision-Making Model for Regional Governance Based on Adaptive Preference Learning Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research develops a dynamic decision-making model for regional governance based on adaptive preference learning to address the limitations of traditional static policy frameworks. The study integrates decision theory, reinforcement learning, Bayesian preference modeling, and multi-criteria decision-making (MCDM) into a unified system capable of capturing evolving stakeholder preferences and responding to rapidly changing socio-economic conditions. The model consists of four core components data input layer, preference learning engine, policy decision module, and real-time feedback system which collectively enable continuous updating of decision parameters and ongoing evaluation of policy outcomes. Using a mixed-method approach that combines stakeholder surveys, historical governance data, performance indicators, and computational simulations, the study demonstrates that the adaptive model significantly improves decision accuracy, responsiveness, and alignment with citizen needs. The system’s dynamic feedback loops allow policies to be refined in real time, enhancing predictive capability and reducing the risks associated with rigid or outdated policy assumptions. Results show that the model outperforms traditional governance approaches in terms of decision efficiency, data-driven fairness, and the ability to anticipate emerging issues. Although challenges remain such as data sparsity, computational complexity, infrastructure limitations, and potential resistance from policymakers the findings highlight the model’s practical value for modern regional governance. The research contributes theoretically by advancing the application of adaptive learning in public policy decision-making and practically by offering a framework that supports faster, smarter, and more citizen-centric governance. Overall, the study underscores the potential of adaptive preference learning to transform regional decision-making in increasingly complex and uncertain environments.
Quantum-Enhanced Cloud Intelligence: Hybrid Variational Models for Next-Generation Scalable Machine Learning Varano, Quentil; Draxmont, Iselphine; Nyvrenn, Zalmera O.
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research investigates a quantum enhanced cloud intelligence framework based on hybrid variational models that integrate Variational Quantum Algorithms (VQAs), Parameterized Quantum Circuits (PQCs), and classical machine learning optimization. The study aims to address the scalability and computational limitations of conventional cloud-based machine learning by leveraging the expressive power of quantum feature spaces and entanglement-driven representations. A structured methodology is presented, encompassing hybrid model design, dataset preparation, quantum circuit construction, and the implementation of a cloud-integrated training loop. Performance benchmarking across high-dimensional datasets demonstrates that the proposed hybrid approach can achieve faster training, improved model accuracy, and enhanced energy efficiency compared to classical baselines. The research further outlines the practical challenges posed by NISQ-era hardware, noise sensitivity, cloud latency, and hybrid optimization instability. Despite these limitations, the findings reveal strong potential for deploying quantum-assisted intelligence in real-time analytics, complex optimization problems, healthcare diagnostics, autonomous systems, and cybersecurity applications. This study contributes a unified integration framework, novel empirical benchmarks, and a practical roadmap for advancing quantum cloud synergy, positioning hybrid variational systems as a promising foundation for next-generation scalable machine learning.

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