Journal of ICT Research and Applications

Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language

Arief Purnama Muharram — 2025-09-15

Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through natural language inference (NLI) to verify the truthfulness of information based on supporting evidence. However, one challenge that arises in deep learning is performance stagnation due to a lack of knowledge during training. This study proposes using a knowledge graph (KG) as external knowledge to enhance NLI performance for automated COVID-19 fact-checking in the Indonesian language. The proposed model architecture comprises three modules: a fact module, an NLI module, and a classifier module. The fact module processes information from the KG, while the NLI module handles semantic relationships between the given premise and hypothesis. The representation vectors from both modules are concatenated and fed into the classifier module to produce the final result. The model was trained using the generated Indonesian COVID-19 fact-checking dataset and the COVID-19 KG Bahasa Indonesia. Our study demonstrates that incorporating KGs can significantly improve NLI performance in fact-checking, achieving a maximum accuracy of 0.8616. This suggests that KGs are a valuable component for enhancing NLI performance in automated fact-checking.

AI-enhanced Cybersecurity Risk Assessment with Multi-Fuzzy Inference

Essam Natsheh — 2025-09-15

The pace and complexity of modern cyber-attacks expose the limits of traditional ‘impact × likelihood’ risk matrices, which compress uncertainty into coarse categories and miss inter-dependent threat dynamics. We propose a three-layer multi-fuzzy inference system (MFIS) that models general infrastructure vulnerabilities and access-control weaknesses separately, then fuses them into a single, continuous 0-25 risk score. The framework was validated on three representative scenarios—catastrophic/continuous, serious/frequent, and minor/few attacks—encompassing sixteen threat criteria. Compared with a crisp 5 × 5 matrix, MFIS cut mean-absolute error and root-mean-square error by 90 to 99% and reproduced expert-panel judgments to within 0.55 points across all scenarios. Nine independent practitioners rated the prototype highly on usability (100% agreement), credibility (100%) and actionability (100%), with 78% willing to recommend adoption. These results demonstrate that MFIS delivers fine-grained, expert-aligned assessments without adding operational complexity, making it a viable drop-in replacement for time- or resource-constrained organizations. By capturing partial memberships and cross-domain interactions, MFIS offers a more faithful, adaptive and explainable basis for prioritizing cyber-defense investments and can be extended to emerging threat domains with modest rule-base updates.

A System Dynamics Model of 5G Low-Band Spectrum Management

Muhammad Shalahuddin — 2025-11-03

The fifth-generation (5G) mobile communication system represents a major advancement in wireless technology, relying on effective radio spectrum management to ensure optimal performance. Among the available frequency ranges, the 5G low-band spectrum provides extensive coverage but limited capacity, making its efficient management a critical challenge. This study presents a predictive model based on the system dynamics approach to analyze the management of the 5G low-band spectrum. The model captures the interrelationships between technical and economic variables that influence spectrum allocation and service adoption over time. Three simulation scenarios—low, medium, and high allocation rates—were developed to examine allocation patterns and their effects on 5G service diffusion. The results revealed that spectrum management in 5G exhibits goal-seeking behavior constrained by spectrum scarcity, with service adoption showing a growth-to-saturation pattern. The findings demonstrate that appropriate low-band spectrum management can significantly enhance 5G deployment efficiency. The proposed model serves as a decision-support tool for policymakers and regulators, enabling evaluation of alternative management strategies prior to policy implementation and promoting evidence-based decision-making in future 5G spectrum policies.

Foundations of Domain-specific Large Language Models for Islamic Studies: A Comprehensive Review

Mohamed Yassine El Amrani — 2025-11-03

Large language models (LLMs) have undergone rapid evolution and are highly effective in tasks such as text generation, question answering, and context-driven analysis. However, the unique requirements of Islamic studies, where textual authenticity, diverse jurisprudential interpretations, and deep semantic nuances are critical, present challenges for general LLMs. This article reviews the evolution of neural language models by comparing the historical progression of general LLMs with emerging Islamic-specific LLMs. We discuss the technical foundations of modern Transformer architectures and examine how recent advancements, such as GPT-4, DeepSeek, and Mistral, have expanded LLM capabilities. The paper also highlights the limitations of standard evaluation metrics like perplexity and BLEU in capturing doctrinal, ethical, and interpretative accuracy. To address these gaps, we propose specialized evaluation metrics to assess doctrinal correctness, internal consistency, and overall reliability. Finally, we outline a research roadmap aimed at developing robust, ethically aligned, and jurisprudentially precise Islamic LLMs.