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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 74 Documents
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Search results for , issue "vol 15, no 5: october 2026" : 74 Documents clear
Development of an electronic repository with data analytics for extension services for the University of Northern Philippines Richard Corpuz Arruejo; Arpee Callejo Arruejo
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11476

Abstract

Submission of documents and reports in higher education extension services often suffers from operational lag due to manual workflows and passive repository platforms. This study developed and tested an electronic repository system (eRXS) equipped with an integrated data analytics-enabled dashboard, automated reporting tools, and communication modules. Rapid application development (RAD) was used as the method for the system development and was tested through system load testing and live benchmarks. Quantitative load testing with 75 trials using a 102-document seed as baseline revealed exceptional technical stability, achieving a total of 15.038 milliseconds and sub-millisecond database retrieval. This robust system performance significantly lowered user cognitive load and facilitated sustainability in system utilization, resulting in an organic 153 document uploads. Consequently, through real-time data visualization, the system reduces administrative information asymmetry, achieving full document compliance across tested samples (n=75). These findings suggest that the developed system successfully transforms standard repositories from simple storage solutions into proactive drivers of compliance and institutional accountability, but further evaluation of user acceptance is recommended for future work. The primary contribution of this work is application-oriented, demonstrating technically validated, domain-specific integration of analytics into extension service repository management-rather than a novel theoretical framework.
Siting solar-fed wireless electric vehicle charging infrastructure using MCDM-AHP frameworks Ankit Balhara; Rakesh Dhiman
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.14264

Abstract

One important component of sustainable urban mobility is the placement strategy of electric vehicle (EV) infrastructure. Through non-conventional energy, this paper presents a techno-managerial approach for the installation of hybrid wireless EV charging stations. This research uses analytic hierarchy process (AHP) to rank three generic approaches: market places, highway transit regions, and educational university zones. Four selection criteria: solar potential, land geometry, traffic throughput, and grid proximity are used to mitigate the problem of “urban canyons” due to shading and scarcity of land. Mathematical validation proves the validity of the proposed method using a consistency ratio (CR) of 0.005. It is evident from the findings that the highway transit region is the most reliable location for hybrid solar-wireless EV charging stations with a global priority of 0.533. The selection criteria such as energy generation potential and linear road have been found more important than high demand density in other approaches.
Artificial intelligence applications in porous asphalt pavement I Gusti Agung Ananda Putra; Darpan Rokade
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12289

Abstract

Porous asphalt pavement (PAP) was developed as a sustainable pavement solution to improve rainwater infiltration, reduce inundation, and improve driving safety. The performance of PAP is influenced by the complex interactions between pore structures, clogging mechanisms, and structural and thermal responses, thus encouraging the application of artificial intelligence (AI) in its modeling. This study aims to comprehensively synthesize the application of AI in PAP through the systematic literature review (SLR) approach. The SLR process was carried out against the publication of the 2021–2026 period from the Elsevier, SpringerLink, and MDPI databases, which resulted in 10 high-quality articles for analysis. The results show that AI enables accurate and non-destructive prediction of hydraulic and clogging performance, while AI-integrated mechanistic models achieve a correlation coefficient of up to 0.994 for structural response and an error below 5% for thermal conductivity. Image-based deep learning and generative models further demonstrate potential for pavement condition assessment and porous microstructure modeling. The novelty of this review lies in integrating evidence on AI applications across the hydraulic, clogging, structural, thermal, and microstructural domains of PAP. The findings highlight the need for an integrated, material-specific AI framework to support sustainable PAP design, monitoring, and maintenance.
Ultra-low-power 2D DCT/IDCT architecture using hybrid GDI-PTL adders and Wallace tree multipliers Bhavya K B; A M Vijaya Prakash
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.13505

Abstract

The discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) are widely used in image and video compression, but their hardware implementation requires substantial arithmetic activity and can increase power and area. This work addresses this issue by developing an ultra-low-power, full-precision 2D DCT/IDCT architecture that combines hybrid gate diffusion input and pass transistor logic (GDI-PTL) full adders with Wallace tree multipliers. The objective is to reduce arithmetic power and area while preserving reconstruction fidelity. The proposed architecture uses separable row- and column-wise processing, fixed-point coefficient representation, pipelining, and fine-grained clock and data gating. The register-transfer level (RTL) design is verified using Verilog and MATLAB, and the application specific integrated circuit (ASIC) implementation is synthesized in Cadence Genus and physically implemented in Cadence Innovus Stylus at 45 nm. Post-layout results show a core area of 58,540.8 µm², total power of 2.874 mW at 100 MHz, and a critical data-path delay of 0.261 ns. Image reconstruction is evaluated using compression ratio (CR), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), demonstrating that the architecture maintains high reconstruction quality while reducing hardware power consumption.
Reliable V2V networks via LED-based Li-Fi technology for road safety applications Mohan P. Thakre; Krupali Kanekar; Badal Kumar; Alok Kumar; Supriya Nilesh Thakur; Pranali M. Thakre; Prashant Magadum
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11398

Abstract

Road traffic accidents remain a significant global challenge, causing substantial loss of life and severe injuries annually. This research proposes a vehicle-to-vehicle (V2V) communication system based on light fidelity (Li-Fi) technology to enhance road safety through low-latency and reliable optical wireless communication. The proposed approach utilizes visible light communication, where light-emitting diodes (LEDs) integrated into vehicle headlights and tail lights act as optical transmitters, while photodiodes installed on receiving vehicles detect transmitted signals. Manchester coding is employed to improve signal integrity by reducing noise effects and enhancing reliable data transmission. The developed system was evaluated through simulation analysis and experimental validation using a hardware prototype and digital storage oscilloscope (DSO) measurements. The results demonstrate successful transmission and reception of digital data with low error rates and consistent agreement between simulation and practical outcomes. The prototype achieved reliable communication over a distance of 15 cm, with performance influenced primarily by transmitter–receiver separation, light intensity, and photodiode sensitivity. The findings confirm that Li-Fi-based V2V communication is a promising, cost-effective, energy-efficient, and low-latency solution for future intelligent transportation systems. Further improvements using LED arrays, advanced modulation techniques, and signal processing methods can extend communication range and enhance system robustness for real-world applications.
Embedding parallel computing readiness in database education: quasi-experimental evidence from Kazakhstan Saule Zhumagulova; Ainur Tokzhigitova; Gulnur Abildaeva; Zhana-Gul Yessendauletova; Leilya Fazylova
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12343

Abstract

Although modern database systems increasingly rely on parallel and distributed execution, parallel computing is often taught separately from database courses. This study addresses this gap by embedding parallel computing readiness directly into database education through a competency-based instructional module. The study designed and evaluated an integrated module that introduced students to parallel execution reasoning through authentic database tasks. A quasi-experimental pre-test/post-test design with an experimental group (EG) (N=81) and a control group (CG) (N=71) was utilized to assess learning outcomes over four competency dimensions: parallel computing readiness, content/theoretical competence, organizational/practical competence, and motivational competence. Clear evidence of a shift from low competency levels to average and high competency levels within the EG was evident across all competency dimensions, with the strongest changes observed in content/theoretical competence, where low-level ratings decreased from 87.7% to 25.9%, and organizational/practical competence, where low-level ratings decreased from 88.9% to 37.0%. In contrast, the CG showed only limited improvement and remained predominantly at the low level across the main competency dimensions. The study provides support for the integration of parallelism with authentic database projects and offers a replicable model for assessing and developing parallel computing readiness in modern database curricula.
Deep learning-based automated brain tumor detection using a proposed convolutional neural network architecture Ghaida Alsharef; Raghad Asiri; Lama Alsultan; Leena Alshehri; Akram M. Zeki; Ahmad AL Moustafa; Amr Mohmed Soliman; Safaa Matter; Mahmoud Khattab
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11511

Abstract

Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologists, are prone to subjectivity and inconsistency. This study proposes a deep learning (DL)-based solution to automate brain tumor detection using a customized convolutional neural network (CNN) architecture tailored for multi-class classification of glioma, meningioma, pituitary tumors, and non-tumor cases. The model incorporates residual blocks, activation functions, and batch normalization to extract and generalize complex spatial features from MRI images. A dataset of 6,000 labeled MRI images was preprocessed and augmented to enhance robustness. The model was evaluated using standard metrics: accuracy, precision, recall, and F1-score. Comparative analysis against ResNet50, InceptionV3, and MobileNetV2 demonstrated superior performance of the proposed model, achieving a classification accuracy of 99.14% and F1-scores of up to 1.00 across some tumor classes. These results validate the model's potential as a reliable and scalable diagnostic aid, offering improved accuracy and reduced diagnostic time. This work sets the foundation for intelligent, accessible, and clinically deployable brain tumor detection systems.
Collaborative trust computation model for social internet of things Rashmi M R; C Vidya Raj; B R Vatsala; Bharathi Ramachandra; Mayura D. Tapkire
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11614

Abstract

Trust compromise is a major problem in social internet of things (SIoT). Existing trust management approaches often rely on predefined trust values, limited behavioral attributes, or historical interactions, making them less effective under dynamic and cold-start environments. This paper proposes a deep collaborative event trust model (DCETM) integrating collaborative event confidence modeling with graph neural networks (GNNs), long short-term memory (LSTM) networks, and multi-attribute trust evaluation to estimate device trustworthiness from spatial, temporal, and behavioral information. The proposed framework derives evidence-driven trust scores from collaborative event observations and integrates reliability, consistency, response behavior, and event confidence for robust malicious node detection. A preference-aware topology refinement mechanism further isolates malicious devices and promotes trustworthy interactions. Experimental evaluation on a SIoT benchmark dataset demonstrates that the proposed DCETM achieves 93.4% attacker detection accuracy, 0.90 F1-score, and reduces the misclassification rate by 25–30% compared with recent deep learning-based trust models. In-addition proposed DCETM maintains reliable trust estimation under cold-start conditions by avoiding default trust assignment through collaborative event validation, demonstrating its suitability for secure and scalable SIoT environments.
Similarity-driven intrusion detection for cloud-centric internet of things networks Jawahar Muthusamy; Palani Latha; Rajakumar Ponnumani; Anubhav Kumar; Rajakumar Perumal; Prabu Kaliyaperumal
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.13882

Abstract

Cloud-centric internet of things (IoT) environments continuously generates heterogeneous network traffic, making intrusion detection increasingly challenging as attack behaviors evolve beyond previously observed patterns. Conventional intrusion detection systems (IDS) rely primarily on supervised classification, limiting their ability to generalize to unseen threats while providing limited explanation for detection decisions. This paper proposes similarity-driven intrusion detection framework (SiamIDS), a SiamIDS that employs behavioral similarity learning to identify malicious network traffic. The framework integrates an autoencoder for feature compression with a Siamese bidirectional long short-term memory (Bi-LSTM) network for temporal similarity modeling, while SHapley Additive exPlanations (SHAP) provide feature-level explanations and ordering points to identify the clustering structure (OPTICS) supports post-detection behavioral analysis of anomalous traffic. The proposed framework was evaluated on the CIC IoT-DIAD 2024 dataset and compared with representative baseline deep learning models. Experimental results achieved an overall accuracy of 99.76%, precision of 95.62%, recall of 98.98%, and an F1-score of 97.24%, demonstrating consistent detection performance across seven attack families. Reconstruction loss analysis confirmed stable latent feature learning, while SHAP explanations showed that multiple network attributes collectively influenced intrusion decisions. These findings demonstrate that behavioral similarity learning provides an accurate and interpretable solution for intrusion detection in cloud-centric IoT environments.
A unified deep learning framework for accurate student performance-prediction towards sustainable education Taneja Sanjay Devkishan; Sanjay Kumar Singh; Ajay Kumar Bharti
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12458

Abstract

Forecasting how students will perform early in their studies is difficult in higher education domain, reason being varying types of academic and behavioral data. Traditional deep learning (DL) techniques face challenges with the selection of influential attributes and feature relationships, resulting in poor performance prediction. To overcome this, our research presents a sequential attribute predictor (SAP) and an attention-enhanced hybrid deep learning (HDL) framework which combines feature-selection using SAP with an attention mechanism and examines five DL models: long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), hybrid LSTM–GRU, and hybrid CNN–GRU. These models were trained on an extensive dataset collected from students of various universities in India and other countries. The assessment results showed that performance of hybrid models is superior to that of individual DL models. The best performing model was SAP based attention-enhanced hybrid CNN–GRU, which had lowest training MSE of 0.0434 and a validation MSE of 0.0284. A comparatively higher coefficient of determination (R²=0.9746) of proposed model clearly reflects a better fit between predicted and actual outcomes due to CNN’s capability to extract discriminative features, GRU’s effectiveness in modeling sequential patterns, and the attention mechanism’s ability to emphasize influential attributes.

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