cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Naety
Contact Email
jurnalmedicom@iocscience.org
Phone
+6281381251442
Journal Mail Official
jurnalmedicom@iocscience.org
Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia
Location
Unknown,
Unknown
INDONESIA
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 130 Documents
 9   10   11   12   13 
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Foundational Study on Integrating Machine Learning with Distributed Computing for Scalable Intelligent Systems Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): 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.Vol17.2025.1391.pp171-182

Abstract

The rapid growth of data-intensive applications and increasingly complex machine learning (ML) models has created an urgent need for computational architectures capable of supporting large-scale intelligent systems. This research presents a foundational study on integrating machine learning with distributed computing to achieve scalable, high-performance AI workflows. The study develops a conceptual integration model comprising four core layers data, compute, communication, and model designed to address scalability, fault tolerance, and resource optimization. Using experimental benchmarking and architectural analysis, the research evaluates multiple distributed frameworks, data partitioning strategies, and ML models to measure improvements in training speed, throughput, latency, and resource utilization across cluster-based and cloud environments. Results demonstrate significant performance gains compared to single-node execution, particularly for deep learning workloads, while also identifying critical bottlenecks such as communication overhead, synchronization delays, heterogeneous hardware constraints, and data imbalance. The findings highlight key trade-offs between accuracy and computational speed, as well as cost and system performance, underscoring the importance of strategic design decisions in large-scale ML deployments. This study contributes theoretical and practical insights into distributed ML integration and offers a framework that can guide the development of next-generation intelligent systems capable of operating across massively distributed environments.
A Unified Mathematical Framework for NWC, MODI, and Stepping Stone as Foundational Models in Optimal Transport Theory Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): 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.Vol17.2025.1393.pp183-195

Abstract

This research introduces a unified mathematical framework connecting three classical transportation problem methods Northwest Corner Rule (NWC), Modified Distribution Method (MODI), and the Stepping Stone Method to the modern theory of Optimal Transport (OT). Despite their long-standing use in operations research, these classical algorithms have traditionally been treated as heuristic procedures without a formal theoretical link to the rigorous Monge Kantorovich formulation. This study demonstrates that each method corresponds directly to fundamental geometric and dual structures of the transportation polytope: NWC generates an initial extreme-point solution, MODI computes dual potentials analogous to Kantorovich potentials, and Stepping Stone identifies improvement cycles consistent with movements along polytope edges. Using formal definitions, algebraic mappings, and geometric interpretation, the research establishes a coherent connection between classical OR algorithms and OT duality theory. The results show that these methods are not isolated heuristics, but structured approximations of optimal transport processes. The unified framework improves theoretical understanding, simplifies instructional explanations, and offers methodological insights that may support future algorithmic enhancements. Limitations include scalability challenges and reduced applicability to complex continuous OT settings. Overall, this research contributes a foundational unification that bridges classical transportation algorithms with contemporary optimal transport theory, advancing both theoretical rigor and practical comprehension.
A Mathematical Framework for Integrating Neural Networks into Stochastic DEA Models to Reduce Variance and Improve Prediction Stability Sihotang, Hengki Tamando; Simbolon, Roma Sinta
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): 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.Vol17.2025.1394.pp196-206

Abstract

This study proposes a novel mathematical framework that integrates neural networks into Stochastic Data Envelopment Analysis (SDEA) to reduce variance and enhance the stability of efficiency prediction under uncertainty. Traditional DEA models rely on linear or piecewise-linear frontiers and are highly sensitive to noise, resulting in unstable efficiency scores and unreliable rankings. The proposed hybrid framework addresses these limitations by combining stochastic frontier modeling, noise-distribution assumptions, and neural network function approximation to construct a smooth, flexible, and noise-resilient efficiency frontier. Neural components capture nonlinear relationships among inputs and outputs, while regularization and bootstrapping techniques stabilize estimation and mitigate variance inflation. Empirical experiments demonstrate that the integrated model outperforms classical DEA, stochastic DEA, and bootstrap-corrected DEA in terms of variance reduction, robustness to noise, and stability across repeated sampling. Efficiency scores exhibit narrower confidence intervals, more consistent DMU rankings, and improved frontier curvature representation. Sensitivity analyses further show that the model remains robust under different noise structures and hyperparameter settings. The findings highlight the potential of combining machine learning with stochastic optimization to advance the methodological foundation of DEA. By enhancing frontier flexibility and reducing noise-induced bias, the proposed framework provides a more reliable tool for efficiency evaluation in complex and uncertain production environments. Future work should focus on enhancing interpretability, reducing computational cost, and relaxing distributional assumptions to further extend the applicability of this hybrid approach.
A Foundational Model for Data-Driven Decision Systems Using Probabilistic Preference Structures Sihotang, Jonhariono; Batubara, J
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): 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.Vol17.2025.1396.pp207-217

Abstract

This research proposes a foundational model for data-driven decision systems based on probabilistic preference structures, addressing the limitations of traditional deterministic and utility-based approaches. The model integrates probability theory, Bayesian inference, and decision theory to represent preferences as flexible probability distributions capable of capturing uncertainty, partial orderings, and multi-attribute trade-offs. A set of novel algorithms is introduced for learning and estimating latent probabilistic preferences from noisy, incomplete, and heterogeneous data sources. These learned preference structures are embedded within an optimization framework that combines Bayesian updating with Markov decision processes, enabling the system to generate optimal decisions under uncertainty. Experimental evaluations conducted across synthetic and real-world datasets demonstrate significant improvements in accuracy, robustness, stability, and decision quality compared to existing preference modeling methods. The unified framework also enhances explainability by quantifying uncertainty and providing interpretable probabilistic outputs. The research makes theoretical contributions by establishing a mathematical ontology for probabilistic preferences, methodological contributions through the development of scalable inference and decision algorithms, and practical contributions by enabling reliable decision-making in environments characterized by inconsistent or probabilistic data. Overall, the results validate the proposed framework as a comprehensive and flexible foundation for next-generation intelligent decision systems, offering improved adaptability, reliability, and transparency in complex real-world applications.
Human-Centered AI for Immersive XR Environments: A Multisensor Fusion Approach for Adaptive Interaction and Cognitive Modeling Meskir, Arvando L.; Juvens, Talira N.; Vorsteyn, Junelle
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): 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.Vol17.2025.1397.pp219-229

Abstract

Immersive Extended Reality (XR) systems are rapidly expanding across education, training, healthcare, and industrial applications, yet most existing frameworks lack real-time adaptivity and personalized support based on users’ cognitive and emotional states. This research proposes a human-centered AI framework that integrates multisensor fusion with cognitive state modeling to enable adaptive and intelligent interaction within XR environments. The system combines data from eye tracking, body and hand motion capture, environmental sensors, audio input, and physiological signals such as EEG, EMG, and HRV. A hierarchical fusion engine performs low-, mid-, and high-level integration of multimodal signals, while deep learning models including CNNs, LSTMs, and multimodal transformers estimate user states related to attention, workload, fatigue, and emotion. The framework dynamically adapts the XR environment through real-time modifications to UI complexity, lighting, haptic feedback, content pacing, and virtual assistant behavior. Experimental results demonstrate substantial improvements in cognitive load prediction accuracy, interaction robustness, and user immersion compared to single-sensor or static XR systems. Users experienced reduced cognitive overload, enhanced task performance, and greater engagement across various simulated tasks.  Overall, this research advances human-centered AI by demonstrating how multisensor fusion and cognitive modeling can transform XR from passive simulation platforms into adaptive, perceptive, and user-responsive environments. The findings offer a foundation for next-generation XR systems that prioritize human well-being, performance, and comfort through continuous AI-driven personalization.
Design and Testing of an Energy-Saving Ultrasonic Rat Repeller Prototype for Open Agricultural Environments Sihotang, Hengki Tamando; Simbolon, Roma Sinta
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rat infestations are a major threat to agricultural productivity in open-field environments, causing significant crop damage and economic losses. Conventional control methods, such as chemical poisons and mechanical traps, are often labor-intensive, environmentally harmful, and pose risks to non-target species. This research focuses on the design, development, and testing of an energy-saving ultrasonic rat repeller prototype tailored for open agricultural fields, aiming to provide an environmentally friendly and practical pest control solution. The prototype integrates a microcontroller-based control system, ultrasonic transducer, and energy-efficient power management, including low-power modes and intermittent frequency emission to reduce energy consumption while maintaining repellent effectiveness. Laboratory testing verified frequency accuracy, operational stability, and power usage, while field testing assessed rat activity reduction, crop damage mitigation, and device endurance under varying environmental conditions. Results indicate that the prototype effectively deters rats within its coverage area, reduces crop damage, and consumes significantly less energy compared to conventional continuous-emission devices. The study demonstrates the feasibility of energy-efficient ultrasonic technology for sustainable pest management and provides a foundation for future enhancements, such as solar-powered operation, IoT-based monitoring, and multi-pest control integration.

Page 13 of 13 | Total Record : 130

 9   10   11   12   13 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 17 No 5 (2025): November : Intelligent Decision Support System (IDSS) Vol 17 No 4 (2025): Intelligent Decision Support System (IDSS) Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS) Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS) Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS) Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS) Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS) Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS) Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS) Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS) Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS) Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS) Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS) Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS) Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS) Vol 15 No 2 (2023): May: Intelligent Decision Support System (IDSS) Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS) Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 13 No 2 (2021): September: Intelligent Decision Support System (IDSS) Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS) Vol 12 No 1 (2020): March: Intelligent Decision Support System (IDSS) Vol 11 No 2 (2019): Informatik Vol 11 No 1 (2019): Informatika Vol 10 No 2 (2018): Informatika More Issue