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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 1 (2025): March: Intelligent Decision Support System (IDSS)" : 5 Documents clear
A Comprehensive Review of Modern Machine Learning Architectures: From Statistical Models to Adaptive Intelligent Computing Systems Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

  This research presents a comprehensive review of the evolution, performance, and limitations of modern machine learning architectures, spanning from classical statistical models to advanced adaptive intelligent computing systems. By systematically comparing diverse architectural families including linear models, tree-based learners, convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformers, and emerging adaptive systems the study evaluates their computational complexity, training efficiency, scalability, data requirements, interpretability, robustness, and adaptability. The findings reveal that while traditional models remain valuable for their simplicity and transparency, deep learning and Transformer-based architectures significantly outperform earlier methods in handling large-scale, high-dimensional, and unstructured data. However, these performance gains come with notable challenges, including high computational and energy costs, adversarial vulnerability, data bias, lack of explainability, and difficulties in deployment on resource-limited devices. The study also compares current results with key findings from the past decade, highlighting both continuities and major advancements in model capabilities, scalability, and reliability. Overall, the research contributes an integrated framework that synthesizes technical, ethical, and practical considerations, offering deeper insights into the strengths, limitations, and future directions of modern machine learning architectures. The study underscores the need for more interpretable, energy-efficient, and ethically aligned AI systems to support responsible and sustainable technological development.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Highly complex systems such as smart grids, autonomous transportation networks, and large-scale supply chains present significant challenges for optimization due to high dimensionality, nonlinear interactions, and pervasive uncertainty. Traditional deterministic models often fail under dynamic conditions, while many AI-based approaches lack robustness and stability when confronted with noisy or incomplete data. Addressing these issues, this study proposes a probabilistic decision model designed to enhance AI-driven optimization in uncertain and rapidly changing environments. The model integrates probabilistic graphical structures, Bayesian inference, and AI-based optimization techniques to quantify uncertainty and support adaptive decision-making. Experimental evaluations were conducted using a combination of synthetic datasets, simulation environments, and benchmark scenarios representative of real-world complex systems. Results show that the proposed model achieves significantly higher decision accuracy, improved stability under noisy conditions, and more efficient performance in high-dimensional settings compared with classical optimization, reinforcement learning, and standard probabilistic approaches. The model consistently reduces uncertainty and delivers robust, reliable solutions across a wide range of test conditions.The study presents a scalable, interpretable, and highly effective framework for uncertainty-aware optimization. Its strong performance and generalizability highlight its potential for deployment in critical real-world applications where reliability, safety, and adaptability are essential.
A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Stochastic Mixed-Integer Nonlinear Programming (MINLP) Systems Sihotang, Hengki Tamando; Simbolon, Roma Sinta
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Highly complex systems present significant challenges for optimization, particularly when operating under uncertainty, high dimensionality, and dynamic environmental conditions. This study proposes a probabilistic decision model designed to enhance AI-driven optimization by integrating uncertainty quantification, adaptive decision mechanisms, and robust probabilistic reasoning. The methodology combines probabilistic modeling with machine learning techniques and is evaluated through a series of controlled experimental scenarios that simulate real-world complexity and noise. The results indicate substantial improvements in decision accuracy, solution stability, and robustness compared to traditional deterministic and heuristic-based optimization methods. The model consistently maintains high performance despite uncertain inputs and fluctuating system parameters, demonstrating its reliability in environments where conventional approaches tend to degrade. Theoretical analysis further validates the model’s feasibility and guarantees performance consistency under uncertainty. Overall, this research contributes a scalable and resilient decision-making framework capable of addressing the limitations of existing optimization models, offering significant potential for broad application in AI-driven complex systems.
A Unified Hybrid AHP, Utility, TOPSIS Decision Model for Enhancing Ranking Reliability in Complex Multi-Criteria Problems Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study proposes a unified mathematical framework that integrates the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Utility Theory to enhance multi-criteria decision-making (MCDM) in complex environments. While AHP provides a structured mechanism for deriving criterion weights, TOPSIS offers an effective geometric ranking approach, and Utility Theory captures nonlinear preferences and risk attitudes. However, these methods often operate independently, resulting in inconsistent rankings and incomplete representation of decision-maker behavior. The proposed framework bridges these gaps by combining AHP-derived weights, utility-transformed criterion values, and TOPSIS proximity measures into an integrated decision function. A numerical case study illustrates the full application of the model, including weight calculation, utility transformation, ideal-solution analysis, and composite scoring. Results show that the unified model produces more stable and discriminative rankings than pure AHP, pure TOPSIS, or pure Utility Theory. Sensitivity and robustness analyses further demonstrate that the integrated approach maintains ranking consistency under variations in weights, normalization methods, and utility parameters. Comparative validation using Spearman correlation confirms strong agreement with established methods while improving resilience to uncertainty. Overall, this research contributes a comprehensive and theoretically grounded MCDM framework that better reflects human judgment, strengthens ranking reliability, and is adaptable to diverse decision contexts. The unified model offers a powerful tool for practitioners and researchers seeking more accurate and robust decision support in multi-criteria environments.
A Federated Multimodal Learning Framework for Privacy-Preserving Intelligent Computing in Large-Scale IoT Ecosystems Veskardin, Lianora; Threyn, Cassandra R.
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid expansion of large-scale Internet of Things (IoT) ecosystems has generated massive volumes of heterogeneous multimodal data, creating new challenges related to scalability, data integration, privacy protection, and real-time intelligence. Traditional centralized learning architectures struggle with communication bottlenecks, privacy regulations, and the complexity of processing diverse data modalities such as sensor signals, audio, video, text, and location streams. Although federated learning (FL) provides a decentralized alternative, existing FL models remain limited in handling multimodal inputs, managing non-IID data distributions, and ensuring strong resilience to adversarial threats. This study proposes a Federated Multimodal Learning Framework that combines probabilistic representation encoding, hierarchical mixture-of-experts fusion, cross-modal consistency regularization, and communication-efficient update scheduling. The framework enables distributed IoT devices to collaboratively learn multimodal representations without sharing raw data, thereby maintaining compliance with GDPR, HIPAA, and other privacy legislation. A probabilistic multimodal embedding mechanism reduces information leakage while supporting dynamic and reliable cross-modal interactions, even under missing or imbalanced modality conditions. Experimental results show that the proposed framework significantly outperforms existing multimodal FL approaches. It achieves higher model accuracy, reduces communication costs by 40-70%, maintains strong privacy protection with minimal performance degradation, and demonstrates enhanced robustness against adversarial attacks. Furthermore, the model provides superior multimodal fusion quality, effectively aligning heterogeneous data streams within federated constraints. Overall, this research delivers a scalable, privacy-preserving, and highly adaptive solution for intelligent computing in modern IoT environments, offering a stronger foundation for real-world applications in smart cities, industrial automation, healthcare monitoring, and next-generation distributed AI systems.

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