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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 75 Documents
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Search results for , issue "Vol 15, No 2: April 2026" : 75 Documents clear
DeepFloyd-IF via diffusion and U-Net based cross-model attention for semantic coherence Veilumuthu, Kowsalya; Chandrasekar, Divya; Parvathi, Sakthidevi Shunmugalingam
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9927

Abstract

Text to image synthesis is getting harder in artificial intelligence, impacting gaming, advertising, and multimedia. The practical use of current Text to Image models is limited by the trade-off between semantic coherence and visual quality. To address this, this work presents stable diffusion cross-modal attention with multi-head attention (SD-CMA-MHA), a framework for the DeepFloyd-IF task. This combines stable diffusion with U-Net based cross-modal attention and multi-head attention (MHA) to improve DeepFloyd-IF, a standard for high quality image synthesis. This allows the model to capture subtle semantic relationships between text and images while dynamically focusing on relevant input features. Experiments on LAION-1.2B and MS-COCO datasets show that the model achieves 80% generation accuracy, 70% text-image alignment similarity and reduced divergence from real images, better than previous methods. This shows that SD-CMA-MHA improves semantic alignment and fidelity. The conclusion is that by enabling more reliable and context aware visual generation, this work not only bridges the gap between text and visual modalities but also has implications for creative industries, education and human-computer interaction.
Towards energy-efficient 5G networks: coordination solutions for macro and pico cells Putri, Hasanah; Munadi, Rendy; Hertiana, Sofia Naning; Hikmaturokhman, Alfin
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10410

Abstract

The increasing demand for high-speed, low-latency connectivity has driven the rapid deployment of fifth-generation (5G) networks. However, the enhanced performance of 5G systems comes at the cost of higher energy consumption, posing a significant challenge to sustainability goals. This study explores energy-efficient coordination strategies for macro and pico cells to optimize power usage while maintaining network performance. By employing a systematic mapping study (SMS) and a systematic literature review (SLR), we analyze current research trends, challenges, and emerging solutions in energy-efficient 5G network design. Key strategies, including AI-driven resource allocation, dynamic spectrum management, and interference mitigation techniques, are examined to assess their effectiveness in reducing energy consumption. The findings highlight the critical role of intelligent coordination mechanisms in achieving a balance between energy efficiency and service quality. This research contributes to the development of sustainable 5G architectures by identifying optimal methodologies for macro- and pico-cell integration, paving the way for greener and more adaptive next-generation networks.
Recency, frequency, quality: novel feature from sentiment analysis for clustering and ranking in tourism big data analytics Saraswati, Ni Wayan Sumartini; Putra, I Ketut Gede Darma; Sudarma, Made; Sukarsa, I Made; Aristamy, I Gusti Ayu Agung Mas
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10709

Abstract

Understanding tourist perceptions has been a key benefit of sentiment analysis in tourism data. However, its outcomes can be further utilized to gain insights into the characteristics of tourist attractions and hotels. This study aims to develop a new feature, called recency, frequency, quality (RFQ), derived from sentiment analysis results to cluster and rank tourist attractions and hotels in Bali. RFQ consists of three components: review recency, review frequency, and review quality. These dimensions reflect the recentness of reviews, the popularity based on the number of reviews, and the review quality measured by the ratio of positive to negative sentiment polarity. Using big data analytics through clustering and ranking, the study finds that the quality of tourist attractions and hotels is primarily concentrated in Badung and Gianyar regencies. More tourist attractions are found in the silver cluster than in the gold, indicating the need to enhance quality. In the hotel sector, the diamond cluster dominates among star-rated hotels, suggesting overall high quality. Budget hotels show fairly good quality, with most falling under the gold cluster.
A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques Imaduddin, Helmi; Yusuf, Siti Agrippina Alodia; Adhantoro, Muhammad Syahriandi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11247

Abstract

Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Energy-efficient spectrum sensing using a novel adaptive hybrid learning for CR-IoT networks Jaronde, Pravin; Vyas, Archana; Gaikwad, Mahendra
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10795

Abstract

The rapid expansion of internet of things (IoT) networks has intensified spectrum scarcity due to the massive growth in wireless device connectivity. Cognitive radio sensor networks (CRSNs) offer a promising solution by enabling dynamic access to underutilized spectrum bands. However, existing spectrum sensing techniques in CRSNs often suffer from high energy consumption, low adaptability, and limited prediction accuracy posing challenges in energy-constrained environments. This paper proposes an energy-efficient spectrum sensing (EESS) framework using an adaptive hybrid learning model (AHLM) that integrates wavelet transform-based signal decomposition (WT-SD), deep reinforcement learning (DRL), entropy-based hierarchical clustering (EHC), and meta-learning-based transfer learning (ML-TLM). WT-SD extracts key spectral features, while DRL with policy-gradient optimization dynamically predicts spectrum availability. The EHC mechanism clusters sensor nodes to minimize redundant sensing, and ML-TLM enhances adaptability with minimal retraining. The proposed model achieves substantial improvements over traditional methods. Experimental results show a 36% reduction in sensing time, 60% lower energy consumption than energy detection (ED) methods, and an 18.3% increase in network lifetime. The model also achieves a probability of detection of 0.998 and accuracy of 98.1%. These results confirm that the proposed EESS-AHLM framework provides a scalable and intelligent solution for energy-aware spectrum sensing in next-generation cognitive radio (CR)-IoT environments.
PSO-tuned bidirectional converter for intelligent electric vehicle charging in vehicle-to-grid and grid-to-vehicle applications Balasubramanyam, Devarakonda; Raja Sekhar, Goda Ganesh; Muni, Tadanki Vijay
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9763

Abstract

Electric vehicles (EVs) can act as distributed energy storage units in smart grids through vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. However, large-scale bidirectional EV charging introduces power quality issues, including harmonic distortion and DC-link voltage fluctuations. This paper presents a PSO-tuned modified dq (MDq) control strategy for a bidirectional EV charging system operating under V2G and G2V modes. A transformer-less bidirectional DC–DC converter and a grid-connected voltage source inverter with an LCL filter are modeled to enable controlled power exchange between the EV battery and the grid. Particle swarm optimization (PSO) is employed to optimally tune the controller gains using a multi-objective fitness function that minimizes grid current harmonics, DC-link voltage error, current ripple, and settling time. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed MDq controller significantly outperforms conventional PI and MDq-PI controllers, achieving a grid current total harmonic distortion (THD) of 2.39% while maintaining stable DC-link voltage and fast dynamic response. The proposed approach enhances power quality, grid stability, and operational reliability, making it suitable for intelligent EV charging in smart grid applications.
Markerless versus marker-based augmented reality: comparative usability insights from a museum context Amali, Lanto Ningrayati; Katili, Muhammad Rifai; Dwinanto, Arif; Rasim, Rasim
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11286

Abstract

Augmented reality (AR) has become a pivotal tool for enhancing museum engagement through interactive experiences. This study developed and evaluated an Android-based AR application for the Popa Eyato Archaeological Museum, comparing marker-based (Vuforia), and markerless (EasyAR) tracking methods. While previous studies often focus on technical development, this research provides empirical insights into usability within the Southeast Asian Museum context. Using a mixed-methods approach with 35 participants, the application was evaluated using the system usability scale (SUS) and handheld augmented reality usability scale (HARUS). Results show that the application achieved an average SUS score of 78, classified as “Good” and “Acceptable”. The comparative analysis reveals that while marker-based AR offers higher stability, markerless AR provides greater flexibility for spatial exploration. However, environmental factors like museum lighting significantly impact markerless tracking performance. These findings offer practical recommendations for museum curators and AR designers in selecting appropriate tracking technologies based on infrastructural constraints and visitor demographics. The study concludes that balancing technical stability with user mobility is essential for optimizing digital heritage preservation and educational engagement in regional museums.
The role of technology acceptance model in evaluating educational games for higher education Rani, Mohamad Firdaus Che Abdul; Adnan, Nor Hafizah; Mansor, Ahmad Zamri; Yunus, Melor Md.
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10322

Abstract

This systematic review investigates the application of the technology acceptance model (TAM) in evaluating the acceptance of educational games within higher education. Educational games have become prominent tools for enhancing learning outcomes through interactive and engaging experiences. Guided by TAM, this review analyzes how key constructs (perceived ease of use (PEOU), perceived usefulness (PU), attitude toward use (ATU), behavioral intention (BI), and perceived enjoyment (PE) influence students’ acceptance of educational games. Using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, relevant studies published between 2020 and 2024 were systematically identified from Scopus and Web of Science (WoS) databases, with 20 empirical studies meeting the inclusion criteria. The synthesis reveals that PU and PEOU are the most significant predictors of acceptance, while hedonic factors such as enjoyment and flow enhance engagement and sustained use. Key challenges include technical limitations, cultural misalignment, inconsistent TAM extensions, and a lack of longitudinal evidence. The review highlights the need for context-aware and inclusive design approaches that integrate both cognitive and affective factors. Overall, the findings position TAM not only as an evaluative framework but also as a design-oriented model for developing effective, engaging, and pedagogically grounded educational games in higher education.
Vertex and edge labelling strategies for graph-based computed tomography image denoising Setiawan, Iwan; Rosiyadi, Didi; Ratianingsih, Rina; Abu, Maulidyani; Baskoro, Edy Tri
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11306

Abstract

Low-dose computed tomography (LDCT) reduces radiation exposure but introduces elevated noise and streak artifacts that degrade structural fidelity. This paper proposes a graph-based LDCT denoising framework that stabilizes graph construction through explicit vertex and edge labelling guided by paired full-dose CT (FDCT) data. The overlapping LDCT patches are modeled as vertices, and FD-guided affinities are used to build a structurally consistent adjacency matrix and a Laplacian spectrum that are less sensitive to noise. Denoising is performed by spectral filtering via spectral graph wavelet transform (SGWT), followed by overlap–add patch aggregation for image reconstruction. Experiments on paired LDCT/FDCT slices (318 pairs) show that FD-guided labelling improves denoising quality compared with conventional filters and non-guided graph baselines. Quantitative results demonstrate higher peak signal-to-noise ratio (PSNR)/structural similarity index measure (SSIM) with improved edge and feature preservation, indicating better structural boundary retention under noise suppression.
Advanced real-time face detection and recognition in MATLAB El Fezzani, Walid; H. Miraz, Mahdi; Bhuiyan, Mohammad Arif Sobhan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9431

Abstract

Face detection and recognition technologies are increasingly vital in security and surveillance. This article covers two main areas with real-time application: the basics of image processing, such as edge detection and filters, and an overview of global methods for face detection and recognition. The Viola-Jones algorithm, based on Haar-like features and a cascade of classifiers, has been utilized for detecting objects within the images. MATLAB’s toolbox has been used to further enhance face detection performance by identifying human facial patterns in webcam-captured frames. For face recognition, the algorithm compares a detected face with reference images, counting zero-valued differences. If these zero elements exceed a certain threshold, a match is confirmed, indicating a high similarity between the captured face and the reference image. This study presents a low-cost MATLAB prototype emphasizing practical, educational demonstration of real-time face analysis.

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