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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 4 (2025): Intelligent Decision Support System (IDSS)" : 5 Documents clear
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.

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