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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 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)" : 5 Documents clear
A Unified Theoretical-Practical Framework for Explainable Machine Learning in Critical Public Sector Applications Sihotang, Hengki Tamando; Simbolon, Romasinta
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid adoption of machine learning (ML) in the public sector has increased the need for transparent, accountable, and trustworthy algorithmic decision-making, particularly in high-stakes domains such as social welfare, healthcare, security, and public administration. However, existing approaches to explainable machine learning (XML) remain fragmented, focusing primarily on technical explanation techniques without integrating the institutional, ethical, and user-centered requirements of government environments. This research aims to develop a unified theoretical practical framework that operationalizes explainability across the entire ML lifecycle for critical public-sector applications. This study adopts a qualitative, multi-stage research design that combines theoretical synthesis, framework construction, and empirical validation through expert assessment and case-based evaluation.The results demonstrate that explainability is a multidimensional construct that extends beyond algorithmic transparency to include contextual risk assessment, adaptive explanation delivery, and governance mechanisms such as auditability, human oversight, and documentation standards. The proposed framework integrates four interconnected layers context analysis, model design and transparency, explanation delivery, and oversight and governance providing a structured pathway for implementing explainable ML systems that meet public-sector standards of fairness, legitimacy, and accountability. Expert feedback and case evaluations confirm that the framework enhances interpretability, reduces misinterpretation risks, and supports more informed decision-making among stakeholders. This research contributes to the advancement of responsible AI in government by offering a comprehensive model that bridges technical methods with policy and practice, paving the way for more transparent and trustworthy ML adoption in public-sector services.
Dynamic Latent State Modeling for Predicting Public Behavior in Digital Ecosystems Panjaitan, Firta Sari; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study proposes a Dynamic Latent State Modeling (DLSM) framework to predict public behavior within rapidly evolving digital ecosystems. As online interactions grow increasingly complex shaped by algorithmic exposure, platform norms, and sociopolitical events traditional static models fail to capture the fluidity and nonlinearity of user behavior. Using a combination of Hidden Markov Models, state-space modeling, and probabilistic clustering, this research identifies latent behavioral states underlying observable digital activities such as posting frequency, sentiment shifts, network engagement, and information consumption patterns. Results reveal four major latent states Passive Observation, Selective Engagement, Active Participation, and Reactive Mobilization each corresponding to meaningful psychological and social modes of online behavior. Transition matrices demonstrate that users shift states in response to contextual triggers including emotional content exposure, social reinforcement, platform incentives, and external offline events. The DLSM framework outperforms baseline machine learning classifiers by capturing temporal dependencies and hidden motivational structures influencing online actions. The study offers important implications for digital governance, policy design, crisis communication, marketing strategy, and misinformation management, particularly in anticipating rapid escalations in public sentiment or mobilization. However, limitations include potential dataset biases, constraints on generalizability across platforms, and challenges in detecting synthetic or automated behavior (bots) embedded within user streams. Overall, the research contributes a robust, interpretable, and dynamic approach to understanding and predicting public behavior in complex digital environments.
Meta-Learning Algorithms for Resource-Constrained Intelligent IoT Devices Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid expansion of the Internet of Things (IoT) requires devices that can operate intelligently in dynamic environments despite severe hardware and energy constraints. Traditional machine learning models deployed on microcontroller-class IoT devices often struggle to adapt to new tasks, handle sensor noise, and maintain accuracy under changing environmental conditions. This research proposes a lightweight meta-learning framework specifically optimized for resource-constrained IoT platforms, combining gradient-based meta-learning techniques with model compression strategies such as quantization and pruning. The objective is to enable rapid few-shot adaptation, reduce computational overhead, and ensure robust performance in real-world IoT deployments. The study adopts a hardware-aware design approach, implementing the proposed model on ultra-low-power microcontrollers such as ARM Cortex-M series and ESP32. A two-phase training pipeline meta-training and on-device fine-tuning is used to evaluate adaptation speed, latency, memory footprint, accuracy, and energy consumption. Experimental results demonstrate that the lightweight meta-learning model adapts to new sensor-based tasks significantly faster than conventional supervised learning models while consuming substantially less energy. The model also shows improved resilience to environmental variations and sensor noise, outperforming baseline TinyML and standard meta-learning architectures under constrained conditions. Despite these promising results, the research identifies limitations related to computational cost, memory usage during adaptation, and the trade-off between model complexity and predictive accuracy. Nonetheless, the findings highlight the potential of meta-learning as a transformative approach for building intelligent, adaptive, and energy-efficient IoT systems. This study contributes to the advancement of TinyML and edge intelligence by providing a practical and scalable meta-learning solution tailored for ultra-low-power IoT devices.
Graph Neural Networks for Reliability Prediction in Smart City Infrastructure Systems Atticus, Christopher; Peyton, Valentino; Maximilia, Maximilia
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Smart city infrastructures such as transportation networks, energy grids, and water distribution systems are increasingly equipped with heterogeneous sensors that generate large-scale, interconnected data. However, predicting infrastructure reliability remains challenging due to the complex spatial and temporal dependencies within these networks. This research proposes a Graph Neural Network (GNN)-based framework designed to model urban infrastructure as a graph consisting of nodes (e.g., intersections, substations, sensors) and edges (e.g., roads, pipelines, power lines), each enriched with multimodal operational features. By leveraging message-passing mechanisms and spatiotemporal GNN architectures, the model effectively learns relational patterns and evolving system dynamics to predict node and edge failure risks. Experimental results show that the proposed GNN significantly outperforms traditional machine-learning models, time-series approaches, and standard neural networks, achieving higher accuracy, lower error rates, and stronger generalization across infrastructure domains. Visual analyses including graph heatmaps, spatial propagation patterns, and critical node detection demonstrate the model’s ability to identify vulnerability clusters and potential cascading failures. The learned graph embeddings provide interpretable insights into system behavior, highlighting key risk factors and influential structural components. The findings suggest major real-world implications, including improved early warning systems, smarter maintenance scheduling, and substantial cost savings for urban management. While the framework’s performance depends on sensor data quality and computational resources, the study highlights the strong potential of graph-based learning to support more resilient, proactive, and data-driven smart city infrastructure management.
Probabilistic Machine Learning Driven Decision Support System for Enhancing Policy Decision-Making Under Uncertainty Ekrem, Adskhan Reyhan
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research examines the role of uncertainty modeling in enhancing the quality, reliability, and adaptability of policymaking. Traditional policy decisions often rely on fixed assumptions that fail to account for the inherent volatility of social, economic, and environmental systems. By integrating probabilistic techniques, scenario analysis, and sensitivity-based evaluation, the study demonstrates how policymakers can better anticipate variability in economic, social, and environmental outcomes. The findings indicate that uncertainty modeling not only improves predictive accuracy but also strengthens policy resilience by revealing hidden risks, alternative pathways, and the range of possible impacts under differing conditions. The research contributes a structured framework for incorporating uncertainty into policy design and evaluation, providing practical tools for evidence-based decision-making. In practice, the model enables policymakers to make more adaptive, transparent, and risk-aware decisions, ultimately transforming traditional deterministic approaches into dynamic strategies capable of responding effectively to complex and unpredictable real-world challenges.

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