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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 3,202 Documents
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Evaluating lexical feature extraction for plagiarism detection in Arabic documents Marwah Alian; Dana Halabi; Hadeel Rida Alshboul
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11767

Abstract

Plagiarism detection is the task of determining whether a document contains parts from other documents by employing different styles of plagiarism, such as copying certain parts and reordering or replacing words with synonyms, without citing the original text owner. This task is important in many applications, and there are two primary types of plagiarism detection methods: external and intrinsic. Plagiarism detection in Arabic documents is challenging because of Arabic’s rich morphological features, lexical variation, and syntactic complexity, which limit the effectiveness of some detection approaches. To address these challenges, this study introduces an external plagiarism detection framework built on an artificial neural network (ANN) model and a lexical feature extraction framework adapted to the linguistic features of Arabic. The proposed framework is evaluated using ExAraPlagDet-2015 benchmark, where a baseline model using support vector machine (SVM) is introduced for comparison. Experimental results demonstrate notable improvements in plagiarism detection performance of the proposed framework compared with SVM and other baseline methods. The proposed framework provides a precision value of 92% and an F-score value of 96%, verifying its effectiveness for Arabic plagiarism detection.
Developing a micro tasking online crowdsourcing platform for handworkers Muhammad Akram Mat Lazim; Farah Nadia Azman
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.9471

Abstract

Online crowdsourcing has emerged as economic solutions for digitally literate modern society. The use of non-standard employees is what defines the gig economy, in which the hired crowds execute short-term, on-demand micro job assignments for a range of people or companies. Despite the existence of several local crowdsourcing app in the market, there is lack of study on the interaction model of a micro tasking online crowdsourcing platform for handworkers such as plumbing and piping, furniture and household items repairs, installation, electrical repairs, house modelling, carpeting and more. Hence, a prototype of virtual middleman system for handworker crowdsourcing is developed. The technical requirements and design principles of a web-based handworker crowdsourcing system are identified. Evaluation results of functionality, usability, interface and compatibility testing are presented. Essentially, this study contributes to the body of literature by granting a transferable technical requirements and economical potential of virtual middleman system for handworker crowdsourcing. However, the system was tested in a controlled environment with a limited user base, which may not fully represent real-world usage scenarios. Hence, subsequent study should incorporate performance indicators such as task completion rate, latency, or long-term user engagement.
Design architecture of interoperability between electric vehicle charging operators and regulators Prasetyo Aji; Riza Riza; Dionysius Aldion Renata; Tisha Aditya Anggraini Jamaluddin; Panca Kurniawan; Dhea Amelia Rianjani; Edward Edward
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11288

Abstract

The rapid expansion of electric vehicle (EV) charging infrastructure has resulted in a fragmented ecosystem characterized by multiple charging station (CS) operators and limited interoperability between operator platforms and regulatory authorities. This fragmentation creates challenges for service continuity, user experience, and evidence-based policy development. To address these issues, this paper proposes a regulatory-centric interoperability framework that enables standardized data exchange across heterogeneous CS management systems. The proposed solution employs a lightweight representational state transfer (REST) application programming interface (API)-based interoperability model in which the regulator assumes a dual role as both verifier and national data aggregator, allowing operators to share essential operational and transaction data without requiring proprietary protocol alignment. The framework demonstrates that secure and standards-compliant data harmonization can be achieved by leveraging existing backend charging data, thereby improving network visibility, regulatory oversight, and coordination among operators. By supporting structured reporting and cross-operator information exchange, the proposed approach enhances service reliability and facilitates informed infrastructure planning. This study contributes a scalable and policy-aligned interoperability foundation that supports the sustainable development of EV charging infrastructure and provides a basis for future extensions toward advanced grid integration and data-driven mobility governance.
Advanced energy storage technologies for renewable power systems: a comprehensive review Tole Sutikno; Ahmad Raditya Cahya Baswara; Watra Arsadiando; Hendril Satrian Purnama; Mohd Hatta Jopri
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.4864

Abstract

The accelerating deployment of renewable energy, particularly solar and wind power, has intensified the need for advanced energy storage systems (ESS) to address intermittency and enhance grid reliability. However, existing reviews often provide fragmented technology comparisons without a unified techno-economic framework for systematic evaluation. This paper presents a comprehensive review of studies published between 2011 and 2025, examining electrochemical, mechanical, thermal, and hydrogen-based energy storage technologies using four standardized metrics: round-trip efficiency (RTE), levelized cost of storage (LCOS), cycle life, and technology readiness level (TRL). Lithium-ion batteries (LIBs) demonstrate the highest maturity and efficiency (RTE: 90–95% and LCOS: 120–200 USD/MWh), whereas sodium-ion batteries offer promising cost reductions of 30–40% through abundant materials and scalable manufacturing. Pumped hydro and compressed air energy storage (CAES) remain suitable for large-scale applications owing to their long service life and scalability, while thermal energy storage (TES) provides cost-effective long-duration storage for concentrated solar power (CSP). Hydrogen-based storage, despite lower RTE (25–45%), offers unique advantages for seasonal energy balancing. Unlike previous reviews, this study integrates techno-economic performance with technology maturity through a TRL-oriented comparative framework, enabling systematic technology assessment based on deployment readiness, economic viability, scalability, and application suitability, thereby supporting informed decision-making and future hybrid energy storage development.
Image copy-move forgery detection: a survey of methods, datasets, and emerging trends Li-xian Jiao; Kok-Why Ng; Hau-Lee Tong
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10796

Abstract

Digital image forgery has become a critical concern in the era of advanced multimedia technologies, where the authenticity of visual content directly affects trust in digital communication, journalism, and law enforcement. Among various forgery techniques, copy-move forgery (CMF) is among the most common and deceptive, as it involves duplicating a region of an image to conceal or misrepresent information. To address this challenge, numerous copy-move forgery detection (CMFD) approaches have been proposed, ranging from block-based and keypoint-based methods to hybrid models and deep learning (DL) techniques. This paper provides a comprehensive review of these approaches, analyzing their strengths and limitations, and evaluating their performance across multiple benchmark datasets. The evaluation considers factors such as image resolution, manipulation types, and robustness against post-processing attacks. By systematically comparing the algorithms and datasets, the study highlights persistent challenges and outlines future research directions. The findings aim to guide researchers in selecting appropriate techniques and inspire the development of more robust CMFD solutions.
Transfer learning-based malnutrition classification using VGG16 and comparative analysis of CNN architectures Ahmad Fauzi; Haerul Yuda Aditiya; Maharina Maharina; Sihabudin Sihabidin; Muhammad Ansari Adista; Iflan Naufal; Natasya Eka Nanda Sonia Puri; Candra Zonyfar
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11346

Abstract

Childhood malnutrition remains a critical public health challenge in developing countries, with Indonesia ranking fifth globally for stunting burden. Traditional anthropometric assessment methods are time-consuming, resource-intensive, and require trained personnel, necessitating efficient computer-based early detection approaches. This study proposes a deep learning-based method for automated nutritional status classification using facial image analysis. We developed and compared multiple transfer learning architectures including visual geometry group 16 (VGG16), densely connected convolutional network 121 (DenseNet121), mobile network version 2 (MobileNetV2), and residual network 50 (ResNet50) for classifying children into three categories: healthy, malnutrition, and stunting. Results demonstrated that VGG16, a simpler architecture trained for only 10 epochs, achieved optimal performance with 91.8% accuracy, and significantly outperforming more complex modern architectures like ResNet50. This finding challenges the conventional assumption that newer, deeper models invariably perform better, and particularly when working with limited medical datasets. The study revealed that longer training durations led to performance degradation due to overfitting, emphasizing the importance of balancing model complexity with dataset characteristics. These findings support the development of practical artificial intelligence (AI)-based malnutrition screening systems suitable for resource-constrained environments, potentially improving early detection capabilities, and public health outcomes in developing regions.
Adaptive multimodal transformer for wildfire spread prediction using feature-weighted attention Parul Dubey; Gunjan Keswani; Amit Mishra; Manjushree Nayak; Md Rashid Mahmood; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11326

Abstract

Hazard modelling technology has evolved quickly in recent years to predict wildfires, which are an important class of natural disaster for accurate forecasting and proactive control. The combination of remote sensing and machine learning has been playing an important role in this area. Yet traditional models fail to accommodate the sophisticated spatiotemporal dynamics in fire-affected areas and tend to be agnostic toward individual predictors. This study utilizes a multimodal dataset comprising normalized difference vegetation index (NDVI), wind vectors, surface temperature, humidity, land cover, and elevation from sources such as moderate resolution imaging spectroradiometer (MODIS), Sentinel-2, ERA5, and shuttle radar topography mission (SRTM). These inputs were normalized within spatial grids, and temporally-aligned for day-ahead prediction. We introduce an adaptive multimodal transformer (AMT) with a feature weighting module (FWM) to adaptively emphasize informative features. The transformer architecture allows for long-range spatial learning, and the FWM strengthens contextual sensitivity. The novelty of this approach lies in its interpretable feature reweighting mechanism for dynamic environmental conditions. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and mean average precision (MAP). Results show that the proposed model outperforms the MA-Net baseline, achieving a 5% improvement in F1-score and a 14.7% reduction in MAE, demonstrating superior accuracy, generalization, and interpretability.
A reconfigurable 38/60 GHz array antenna for 5G applications Ahmed El Ghandour; Mohamed Nabil Srifi; Younes Karfa Bekali; Mourad El Habchi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10704

Abstract

The aim of this paper is to propose a four-element frequency-reconfigurable antenna array operating at 38 GHz and 60 GHz for 5G applications. The proposed structure integrates three radiating elements into a single patch, enabling compactness and improved performance. The antenna achieves dual-band reconfigurability with gains of 12.65 dBi and 15.54 dBi at 38 GHz and 60 GHz, respectively. Excellent impedance matching is obtained, with S11 values of -51.82 dB and -33.79 dB at the two resonant frequencies. Frequency switching is controlled through positive–intrinsic–negative (PIN) diodes embedded in the ground-plane slot. The antenna design was modeled and validated through full-wave simulations using computer simulation technology (CST) studio suite.
Video summarization based on multi level deep features using convolutional neural network Bineesh Balachandran Nair; Sivarama Krishnan Shunmugan
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10944

Abstract

A video summarization (VIDE SUM) system can be used to condense long hours of footage into short relevant summaries. Summaries generated automatically may fail to capture this subjective relevance, resulting in unsatisfactory summaries. To overcome this a novel, VIDE SUM has been proposed for multi-level deep features-based VIDE SUM. It comprises five main contributions, including deep learning (DL), spearman coefficient (SC), entropy, HAECS, and complexity feature based on multi-quality compression (CMQC). A convolutional neural network (CNN) is used to extract the crucial spatial and semantic information from the frames. The deep four features are extracted to support the effective keyframe selection. The CMQC another innovative feature, is designed to determine the degree of difficulty between two consecutive video frames. In fusing the selected keyframes and relevant features, the resultant video summary preserves the fundamental components of the original video. The proposed VIDE SUM method generates the maximum F1-score of 0.8907, whereas the next-best method, namely VS-GSDA, yields 0.8518, which reveals the power of the proposed method. The evaluation results prove the superiority of the proposed method.
Performance assessment of linear regression for crop yield prediction in comparison with contemporary models Imran Ahmad; Rahul Khokale; Nisha Gongal
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11298

Abstract

Accurate crop yield prediction requires both precision and interpretability for practical agricultural decision-making. This study is the first to systematically benchmark linear regression (LR) against gradient boosting baselines (XGBoost, LightGBM) alongside random forest (RF), support vector machine (SVM), and artificial neural network (ANN) for Indian agricultural yield forecasting, demonstrating that interpretable models can match or outperform black-box approaches. Using a comprehensive Indian agricultural dataset spanning multiple crops, states, and growing seasons (2000–2022), comprising 58,000 records across 22 states and 35 crop varieties, we analyzed features including cultivated area, rainfall, fertilizer applications, and pesticide usage. LR achieved R2 of 0.401 0.02 across 10-fold cross-validation, MSE of 480,239, MAE of 139.50, and RMSE of 692.99, outperforming complex models while maintaining full transparency. Transparent models allow policymakers to understand the impact of rainfall and fertilizer use, enabling evidence-based resource allocation. Results demonstrate that model complexity does not guarantee superior agricultural predictions, and LR provides interpretable coefficients—such as embedding LR coefficients into advisory tools to guide fertilizer recommendations and irrigation scheduling-enabling actionable agronomic insights for sustainable farming practices.

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