1 10 5 https://repository.horizon.ac.id/files/original/b2f0314dcfdb41429e7f82393703cd5c.pdf c203f5ef6e1434e58bdd9c8f8976284d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 19 NO. 1 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Bandwidth Optimization of Spline-Based Planar Sensor Using GA, PSO, and CMA-ES for EMC Testing and Wireless Communications Subject The topic of the resource bandwidth optimization; covariance matrix adaptation–evolution strategy (CMA-ES); electromagnetic compatibility (EMC); genetic algorithm (GA); particle swarm optimization (PSO); planar sensor; wireless communication Description An account of the resource The expansion of communication technology and the increasing usage of the frequency spectrum drive the need for compatible device testing. Wideband antennas play a crucial role in supporting modern communication systems and applications, including those used as the sensors in electromagnetic compatibility (EMC) testing. Optimization techniques, such as genetic algorithm (GA), particle swarm optimization (PSO), and covariance matrix adaptation–evolution strategy (CMA-ES), are widely applied to enhance the bandwidth of electromagnetic devices. However, most studies focus on individual algorithms or limited comparisons, resulting in a lack of systematic evaluation within a unified framework. This paper fills that gap by directly comparing GA, PSO, and CMA�ES on the same planar sensor design, assessing their effectiveness in achieving the widest bandwidth. The planar sensor had a basic spline-based configuration using quadratic Bezier equation. A performance comparison based on a simulation showed that the planar sensor configuration with the best bandwidth was 17.77 GHz, spanning a frequency range from 2.23 GHz to 20 GHz, which was limited by the highest observation frequency of the available measuring instrument. Furthermore, verification of the realized planar sensor showed that the bandwidth reached 17.86 GHz, from 2.14 GHz to 20 GHz, with a geometric bandwidth of 273% Creator An entity primarily responsible for making the resource Agus Dwi Prasetyo, Deny Hamdani, & Achmad Munir Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.5 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 3 November 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource SSN: 2337-5787 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text bandwidth optimization covariance matrix adaptation–evolution strategy (CMA-ES) electromagnetic compatibility (EMC) genetic algorithm (GA) particle swarm optimization (PSO) planar sensor Wireless communication https://repository.horizon.ac.id/files/original/7125b837829cdbe2a68b8857d463f92e.pdf 29e65c2e8c3ab251f1ef1a9b41c9b404 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 19 NO. 1 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Foundations of Domain-specific Large Language Models for Islamic Studies: A Comprehensive Review Subject The topic of the resource bias mitigation; ethical AI; fiqh; Islamic studies; large language models; natural language processing; transformer architecture. Description An account of the resource 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. Creator An entity primarily responsible for making the resource Mohamed Yassine El Amrani, Arshad Vakayil, Feroz Mohammed ,Faisal Al Amri Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.4 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 22 October 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text bias mitigation ethical AI fiqh Islamic studies large language models natural language processing Transformer architecture https://repository.horizon.ac.id/files/original/7b93e88641719190341aeb7ab90faf12.pdf 3ebc830240e53402f7b9a8298eaf5ef3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 19 NO. 1 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A System Dynamics Model of 5G Low-Band Spectrum Management Subject The topic of the resource 5G low-band; goal-seeking; predictive model; radio spectrum management; system dynamics Description An account of the resource 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. Creator An entity primarily responsible for making the resource Muhammad Shalahuddin, Wikan Danar Sunindyo, Mohammad Ridwan Effendi, & Kridanto Surendro Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.3 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 1 October 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN: 2337-5787 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text 5G low-band adio spectrum management goal-seeking predictive model system dynamics https://repository.horizon.ac.id/files/original/31bea843b21a9457f8859aa86e0eae69.pdf 2fdba157bf5d8f694693815970e001b5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 19 NO. 1 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language Subject The topic of the resource COVID-19; deep learning; fact-checking; natural language inference; knowledge graph; natural language. Description An account of the resource 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. Creator An entity primarily responsible for making the resource Arief Purnama Muharram & Ayu Purwarianti Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.2 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 13 August 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN-2337-5787 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text COVID-19 deep learning fact-checking Knowledge Graph natural language natural language inference https://repository.horizon.ac.id/files/original/863d29aedf60870aa04c541e75784e4c.pdf 26cc1f924691ec157b9fc70cfe5d085f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 19 NO. 1 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AI-enhanced Cybersecurity Risk Assessment with Multi�Fuzzy Inference Subject The topic of the resource cybersecurity risk assessment; fuzzy logic; multi-fuzzy inference system; expert validation; adaptive decision support. Description An account of the resource 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. Creator An entity primarily responsible for making the resource Essam Natsheh & Fatima Bakhit Tabook Source A related resource from which the described resource is derived DOI : https://doi.org/10.5614/itbj.ict.res.appl.2025.19.1.1 Publisher An entity responsible for making the resource available IRCS-ITB Date A point or period of time associated with an event in the lifecycle of the resource 18 July 2025 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN : 2337-5787 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text adaptive decision support cybersecurity risk assessment expert validation Fuzzy Logic multi-fuzzy inference system