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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 302 Documents
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A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data Ren-Jieh Kuo; Maya Cendana; Thi Phuong Quyen Nguyen; Ferani E. Zulvia
Journal of ICT Research and Applications Vol. 18 No. 2 (2024): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.18.2.2

Abstract

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI.
Analytical Approach to Parameter Determination in Kaiser Function for Power-weighted Antenna Array Design Hartuti Mistialustina; Chairunnisa Chairunnisa; Achmad Munir
Journal of ICT Research and Applications Vol. 17 No. 1 (2023)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.1.8

Abstract

Window methods that are frequently used in the design of finite impulse response filters are also applicable to antenna array designs. This paper explores the application of a Kaiser function in a power-weighted antenna array design, focusing on the determination of the Kaiser function’s β parameter. The determination, which includes the calculation, optimization, and validation of the β parameter, was carried out based on a specific configuration of a linear antenna array design. The observation of this exploration emphasized the suppression of the sidelobe level (SLL) and the width of main lobe (WML) performance. By changing the β parameter, the Kaiser function is capable of approximating different window methods, since it plays an important role in defining the set of weighting coefficients for a specifically targeted SLL. Kaiser function application in power-weighted antenna array designs with a linear arrangement indicates the need of β parameter optimization because of the disagreement between the obtained SLL and the targeted SLL. The optimized β parameter produced a smaller SLL error for even and odd numbers of elements. From the validation, the average SLL error percentage for a targeted SLL of 25 dB, 35 dB, and 45 dB was 6%, 4.31%, 6.10%, respectively.
A System Dynamics Model of 5G Low-Band Spectrum Management Shalahuddin, Muhammad; Sunindyo, Wikan Danar; Effendi, Mohammad Ridwan; Surendro, Kridanto
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.3

Abstract

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.
Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language Muharram, Arief Purnama; Purwarianti, Ayu
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.2

Abstract

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 Natsheh, Essam; Tabook, Fatima Bakhit
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.1

Abstract

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.
Foundations of Domain-specific Large Language Models for Islamic Studies: A Comprehensive Review El Amrani, Mohamed Yassine; Vakayil, Arshad; Mohammed, Feroz; Al Amri, Faisal
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.4

Abstract

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.
Bandwidth Optimization of Spline-Based Planar Sensor Using GA, PSO, and CMA-ES for EMC Testing and Wireless Communications Prasetyo, Agus Dwi; Hamdani, Deny; Munir, Achmad
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.5

Abstract

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%.
An Intelligent System for Predicting Breast Cancer (ISPBC) using a Novel Feature Selection Technique Das, Akhil Kumar; Biswas, Saroj Kr.; Mandal, Ardhendu; Bhattacharya, Arijit; Saha, Debasmita
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.2

Abstract

Breast cancer (BC) is becoming a global epidemic, largely affecting women. Breast cancer cases keep climbing steadily. Thus, early detection technologies or systems that notify patients to this disease are essential. Individuals can start treatment for this life-threatening illness, so that patients may be cured or given longer lives. To achieve this, in this study, an expert intelligence system named Intelligent System for Predicting Breast Cancer (ISPBC) was developed. The proposed system utilizes an innovative feature selection technique known as Enriched Feature Set (EFS) in order to identify the most appropriate and significant features. The proposed EFS employs the advantages of heuristic search techniques and stochastic hill climbing to select the most significant and important features. The Decision Tree and Random Forest techniques are employed for breast cancer diagnosis, distinguishing between malignant and benign types. The suggested model’s performance was evaluated by comparing measures such as accuracy, precision, and recall through the utilization of tenfold cross-validation. To measure the efficacy of the suggested model, ISPBC’s performance was compared to that of base classifiers and models published in the literature. A maximum accuracy of 96.09% was attained by ISPBC according to the results.
Water Filtration Machine with Monitoring System for Aquades Production and Founding an Optimal Pre- treatment Filter Ratio Before Reverse Osmosis Membrane Adziimaa, Ahmad Fauzan; Rasyiid, Muhamad Sultan
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.4

Abstract

The increasing demand for distilled water (Aquades) in pharmaceutical and medical applications contrasts sharply with the limited quality of municipal water supplies and the high operating costs of commercial Aquades procurement. At the same time, many small-scale facilities still lack integrated systems capable of meeting the Indonesian Ministry of Health standard (Permenkes RI No. 32/2017). Existing research on reverse osmosis (RO) systems largely focuses on membrane or filtration performance, with limited attention to real-time water-quality monitoring and systematic optimization of pre-treatment filters. This study develops an integrated filtration and monitoring system designed to ensure regulatory compliance while optimizing the composition of pre-treatment materials. The system combines silica sand, activated carbon, and zeolite pre-filters with RO, supported by six analog sensors that continuously monitor pH, turbidity, and Total Dissolved Solids before and after filtration. Validation results show high sensor accuracy, with 99.77% for TDS, 98.10% for pH, and 99.97% for turbidity. Among six tested filter compositions, the 25% silica sand-25% activated carbon-50% zeolite configuration achieves the highest average filtration efficiency of 88.96%. These findings demonstrate that optimized pre-treatment combined with real-time monitoring can significantly improve RO performance and support cost-effective Aquades production for medical use.
Scalable and Efficient Student Behavior Prediction using Parallelized Clustering and AHP-weighted KNN Guozhang, Li; Alfred, Rayner; Pailus, Rayner; Fengchang, Xu; Haviluddin, Haviluddin
Journal of ICT Research and Applications Vol. 19 No. 2 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.2.3

Abstract

This study proposes a scalable and efficient approach for predicting student behaviour in large-scale educational environments. It introduces a parallelized hybrid model that combines Density-Based Optimized K-Means clustering, Analytic Hierarchy Process (AHP) feature weighting, and Hierarchical K-Nearest Neighbours (KNN), implemented using Apache Spark. The main research question is how to improve scalability, accuracy, and computational efficiency of student behaviour prediction when dealing with large, complex datasets. The model addresses key limitations of traditional methods, such as handling heterogeneous data, treating all features equally, and high computational cost. Two main innovations are presented. First, AHP is used to assign structured importance to features, allowing critical factors like attendance and study time to have greater influence on prediction accuracy. Second, clustering and prediction are parallelized using Spark, enabling efficient real-time processing of large datasets. The approach was evaluated using 18,586 student records and more than 20 million behavioural entries. Results show that Hierarchical KNN consistently outperforms standard KNN as dataset size increases. While traditional KNN shows unstable error rates, peaking at 9.4%, Hierarchical KNN maintains lower and more stable errors between 5.16% and 6.08%. Execution time was also significantly reduced through parallel processing, though gains were limited by communication overhead. Overall, the proposed model offers a robust framework for real-time behaviour analysis, academic risk detection, and targeted educational intervention.

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