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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 130 Documents
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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.
A Systematic Literature Review on the Theoretical Foundations of Machine Learning in Intelligent Computing Systems Payton, Henry Quinn; Shiloh, Thomas
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study presents a comprehensive theoretical review of the foundations that underpin modern intelligent computing systems, integrating perspectives from statistical learning theory, computational learning theory, optimization theory, information theory, probabilistic modeling, neural computation, and cognitive as well as bio-inspired approaches. Using a systematic review methodology supported by structured search strings and rigorous data extraction, the study identifies core theoretical constructs including VC dimension, PAC learning, sample complexity, entropy, mutual information, Bayesian inference, convergence principles, and universal approximation that collectively shape the development, capabilities, and limitations of intelligent systems. The analysis reveals how these theories complement one another in addressing challenges related to generalization, learnability, optimization efficiency, uncertainty modeling, and biological plausibility. The findings highlight that existing theoretical frameworks provide strong foundations but remain limited in explaining the behavior of high-dimensional, non-convex, and black-box models common in deep learning. The review contributes an integrated conceptual map that clarifies how different theories support robust system design and identifies gaps that future research must address, including scalability of theoretical guarantees, unified frameworks for hybrid systems, and deeper mathematical understanding of modern neural architectures. Overall, the study offers a coherent synthesis that strengthens theoretical grounding and guides future advancements in the construction of reliable and intelligent computing systems.
A Mapping Study on Dynamic Latent State Models in Behavioral and Psysiological Prediction Research Edward, Alexander Grant
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Dynamic Latent State Models (DLSMs) have become increasingly central to behavioral and physiological prediction due to their ability to represent hidden psychological states and temporal dynamics that static machine-learning models cannot capture. This research conducts a systematic mapping study to analyze the evolution, methodological trends, application domains, and dataset usage of DLSMs published over the last decade. Using a structured search strategy across major scientific databases, studies were screened following PRISMA guidelines, and relevant information was extracted to construct a comprehensive taxonomy of model types, signal modalities, and prediction tasks. The results reveal a significant rise in the adoption of DLSMs, particularly after 2018, driven by advances in deep generative models such as deep Kalman filters and variational state-space models. EEG, HRV, and EDA emerge as the most dominant physiological signals, while stress, emotion, and fatigue prediction constitute the primary application areas. Benchmark datasets including DEAP, WESAD, and DREAMER are frequently used but remain limited in ecological diversity, indicating a continuing need for more realistic, multimodal datasets. Comparison with earlier research shows a shift from interpretable probabilistic models toward more expressive but less transparent deep latent models. This study contributes a consolidated overview of theoretical foundations, research patterns, and methodological gaps in the field. The findings highlight key challenges related to interpretability, dataset diversity, and evaluation consistency, while identifying opportunities for hybrid modeling approaches and more comprehensive data resources. Overall, this mapping study provides a structured foundation to guide future work in advancing dynamic latent-state modeling for behavioral and physiological prediction.
Explainable AI for Public Sector Decision Making: A Systematic Literature Review Karl, Roland Vincent
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The growing adoption of Artificial Intelligence (AI) in government has intensified the need for transparent, accountable, and trustworthy decision-making systems. This study conducts a systematic literature review to examine how Explainable AI (XAI) is applied within the public sector, identify the dominant techniques used, and analyze their benefits and challenges. Using PRISMA guidelines, studies were collected from major academic databases including Scopus, Web of Science, IEEE Xplore, SpringerLink, ACM Digital Library, and Google Scholar. The findings reveal that XAI development in government contexts has grown significantly over the past decade, with SHAP, LIME, decision trees, counterfactual explanations, and rule-based models emerging as the most frequently used methods. These techniques support public-sector decision making by enhancing transparency, strengthening accountability, reducing bias, improving auditability, and fostering public trust. However, persistent challenges remain, including technical complexity, trade-offs between accuracy and interpretability, limited AI literacy among officials, lack of standard frameworks, and legal or ethical risks. The review highlights the need for more domain-specific XAI guidelines, user-centered explanation tools, and integrated evaluation frameworks. This research contributes a comprehensive synthesis of current XAI applications in government and outlines a future research agenda to support the development of responsible, explainable, and ethically aligned AI for public administration.
A Comprehensive Review of Machine Learning Paradigms for Large-Scale Smart System Liam, Morgan Jaden
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Large-scale smart systems such as smart cities, smart grids, smart healthcare, and IoT-based infrastructures generate massive volumes of complex, heterogeneous data that require intelligent analysis and real-time decision-making. Machine learning (ML) plays a central role in enabling these capabilities, yet the diversity of ML paradigms and the fragmented nature of existing studies make it difficult to determine which approaches are most effective for large-scale environments. This comprehensive review synthesizes and compares major ML paradigms, including supervised learning, unsupervised learning, reinforcement learning, deep learning, hybrid models, federated learning, and graph-based neural networks, across a wide range of smart system applications. The findings reveal that deep learning excels in processing high-dimensional and unstructured data, reinforcement learning performs best in autonomous and real-time control tasks, federated learning supports privacy-preserving analytics in distributed IoT ecosystems, and graph-based models offer superior performance in systems with interconnected network structures. The review also identifies key technological challenges such as data heterogeneity, computational complexity, communication bottlenecks, and privacy concerns that affect the scalability and deployment of ML in smart environments. By providing a unified comparison of ML paradigms and highlighting emerging trends, performance characteristics, and implementation challenges, this study offers valuable insights for researchers, system designers, engineers, and policymakers. The review further outlines future research directions aimed at enhancing scalability, robustness, interpretability, and real-time capability in next-generation smart systems.
Determining Initial Centroid in K-Means using Global Average and Data Dimension Variance Bu'ulolo, Efori
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The selection of the right initial centroid greatly affects the quality of clustering results in the K-Means algorithm. This study proposes a new approach in determining the initial centroid by utilizing the global average and variance of data dimensions. The global average is used to represent the overall center position of the data, while the variance of dimensions provides information on the distribution of each feature. This method is tested using three-dimensional synthetic data (X, Y, Z) with 121 data, and compared with the random initialization approach. The results show that the global average and variance-based method produces more balanced clusters, lower Sum of Squared Error (SSE) values, and the highest Silhouette Score value (0.65), as well as faster convergence. Compared to two random initialization scenarios, this method is proven to be more stable in separating clusters based on the distribution of low, medium, and high values. This approach makes an important contribution to the development of a more consistent and effective K-Means initialization strategy, especially for low to medium-dimensional numerical datasets.
OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK ALGORITHMIC ACCURACY FOR THE IDENTIFICATION OF DIFFERENT FONT TYPES Kanata, Bulkis; Misbahuddin; Akhdan, M. Rafif
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Text not only conveys the message through the words used, but also through its visual aspects. One of the most influential visual elements is the type of font. Recognising and determining font types appropriately is essential, whether in the academic sector, the printing industry, graphic design, or digital systems. However, in practice, manually recognising font types takes time, skill, and high precision. With the advancement of digital technology, the variety of font types is increasing, making the process of identifying fonts more complicated. This requires the development of methods that are able to distinguish different types of fonts precisely and accurately. This study reveals the potential of Convolutional Neural Network (CNN) algorithms as an optimisation in facing font identification challenges, as well as to prove that deep learning can provide more efficient and precise solutions, by comparing three different CNN architectures, namely DenseNet121, ResNet50, and VGG16. The implementation of the method is carried out by applying data augmentation techniques and setting CNN parameters such as the number of epochs, learning rate, batch size, Adam optimiser, and image size. The results showed that the DenseNet121 model achieved an accuracy of up to 96.8%, ResNet50 92.9%, and VGG16 96.4%. The convolutional neural network algorithm proves that it can identify various font types with optimal accuracy.

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