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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
Transfer learning-based texture-enhanced convolutional neural networks over plant disease identification Thorat, Nilesh N.; Salunke, Mangesh D.; Pimpalkar, Aarti P.; Gulame, Mayuresh B.; Bbhagat, Babeetta; Hirve, Sumit; Saudagar, Saleha; Sanap, Madhura Eknath
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.v15i2.10510

Abstract

The global agricultural productivity and food security take serious threats due to the presence of plant diseases; thus, early and accurate diagnosis becomes the key to successful management of the disease. The traditional diagnosis techniques that rely on visual observation are time-based, subjective, and cannot be implemented on a large scale. Recent development in machine learning and computer vision provides possible solutions to automated plant disease detection. This paper suggests a plant disease identification with transfer learning (PDD-TL) model with the preprocessing, segmentation, feature extraction, and disease prediction phases. In the initial stages, median filtering is used to simplify the image quality, after which cells affected by the disease are segmented with the help of the integration of adaptive pixels in joint segmentation (IAPJS) algorithm. Multi-texton and pyramid histogram of oriented gradients (PHOG) are the discriminative features extracted. The classification of the disease is done with a new triple convolutional activation CNN with transfer learning (CNN-TCA-TL). In contrast to the current methods that use either a pure deep learning method or handcrafted features, the framework proposed explicitly employs both the use of texture descriptors and transferable deep representations, which retain fine-grained structural details. The experimental findings prove that CNN-TCA-TL has an accuracy of 0.92 which will prove that it is effective.
Performance evaluation of a novel blockchain consensus mechanism (PoDIPA) for decentralized microgrid networks Syamsuddin, Sadly; Manjang, Salama; Nappu, Muhammad Bachtiar; Paundu, Ady Wahyudi
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.v15i2.9857

Abstract

The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
Comparative analysis of ResNet backbones in single shot detector for visual-based waste detection Salsabila, Zahra Khalila; Prakisya, Nurcahya Pradana Taufik; Liantoni, Febri
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.v15i2.10540

Abstract

Waste has become a serious environmental issue that requires effective and efficient management systems. This study compares three residual network (ResNet) variants (ResNet-34, ResNet-50, and ResNet-101) within the single shot detector (SSD) framework for visual waste detection. The dataset consists of 800 images in four categories—food, plastic, paper, and wood—with a 70:20:10 split for training, validation, and testing. The backbone architecture, optimizer (stochastic gradient descent (SGD) and Adam), and learning rate are varied to evaluate fifteen experimental configurations. Model performance is assessed using precision, recall, F1-score, and mean average precision (mAP). The results show that SSD–ResNet-34 with SGD and a learning rate of 0.0005 works best, with a mAP of 91.02%, which is better than deeper backbones. Deeper backbone architectures do not consistently improve accuracy; instead, they increase the risk of overfitting on small datasets. These findings highlight that lightweight architecture, when used with the right hyperparameter settings, strikes a better balance between accuracy, computational efficiency, and generalization for small-scale waste detection tasks.
Hybrid ARMA-LSTM model for adaptive link prediction in dynamic underwater sensor networks Bhardwaj, Ritu; Kush, Ashwani
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.v15i2.11114

Abstract

Underwater wireless sensor network (UWSN) is highly vulnerable to packet loss due to varied features of underwater channels, including multipath fading, high latency, and environmental interference. Accurate prediction of packet loss is critical for improving data reliability and network performance. Our research presents a new approach to forecasting using a combination of autoregressive moving average (ARMA) and long short-term memory (LSTM) networks which are statistical models. A synthetic dataset was generated to facilitate model development and evaluation, simulating realistic UWSN conditions by varying key parameters such as signal-to-noise ratio (SNR), received signal strength indicator (RSSI), depth, distance, and temperature. The ARMA model captures linear temporal trends, while the LSTM network is trained on the ARMA residuals to learn nonlinear correction patterns. The findings indicate that the hybrid ARMA-LSTM model exhibits a marked superiority over the standalone ARMA model, achieving an approximate 85.4% reduction in mean absolute error (MAE), an 83.6% enhancement in root mean square error (RMSE), a significant boost in predictive accuracy as reflected by the R² score, which improved from -43.93 to -0.20. The results highlight the hybrid method a strong and precise solution for predicting packet loss in UWSN, directly impacting the improvement of reliability in underwater communication.
A modified one-to-one algorithm for optimizing sustainable lot sizing multi-item models Utama, Dana Marsetiya; Rafika, Yuan; Dewi, Shanty Kusuma
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.v15i2.9844

Abstract

Multi-item inventory management in modern production environments faces major challenges related to stochastic demand, transportation costs, and carbon emissions. This study aims to develop a sustainable lot sizing model that integrates economic and environmental aspects, and proposes the one-to-one based optimization (OOBO) algorithm as a problem-solving approach. The methodology used includes non-linear programming (NLP) formulation that considers stochastic demand, ordering and storage costs, carbon emissions, energy consumption, and vehicle capacity constraints. The model is then optimized using OOBO and compared with the Aquila, particle swarm optimization (PSO), and genetic algorithm (GA) algorithms in three case scale scenarios (6, 30, and 50 items). The experimental results show that OOBO consistently outperforms the comparison algorithms, with cost savings of up to 40.9% in the 50-item case. OOBO also demonstrated high exploration resilience without premature convergence and competitive computational time efficiency. These findings confirm that OOBO is effective in simultaneously optimizing total costs and carbon emissions, making it an adaptive solution for sustainable supply chain management. The theoretical implications include the expansion of OOBO's application to multidimensional stochastic systems, while in practical terms, this model supports decision-makers in formulating environmentally friendly and efficient inventory policies.
Anonymization techniques for privacy-preserving data publishing: a comprehensive survey Smadi, Sami; Karim, Nader Abdel; Kanaker, Hasan; Abdulraheem, Waleed K.
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.v15i2.11224

Abstract

Data-driven innovation in healthcare, finance, and smart cities increasingly depends on sharing rich datasets, but such sharing raises severe privacy risks and regulatory challenges. Privacy-preserving data publishing (PPDP) seeks to release useful data while preventing re-identification and inference attacks. This paper presents a comprehensive survey of anonymization techniques for PPDP, spanning traditional models (k-anonymity, l-diversity, t-closeness, and pseudonymization) and modern approaches (differential privacy (DP), synthetic data generation, federated learning (FL), secure multi-party computation (SMPC), homomorphic encryption (HE), blockchain-based schemes, and quantum-safe cryptography). We propose a taxonomy that organizes these methods by privacy guarantees, data utility, scalability, and computational cost, and we provide a comparative analysis of their strengths, limitations, and typical application domains. The survey also reviews legal and ethical frameworks, with particular attention to general data protection regulation GDPR, health insurance portability and accountability act (HIPAA), and related regulations, and highlights emerging trends such as artificial intelligence (AI-driven) anonymization and privacy risks from large language models (LLMs) and quantum computing. Overall, the study shows that various techniques fail to protect all data scenarios so we need to create hybrid systems which will provide explainable anonymization solutions at different scales to protect privacy and maintain useful data utility.
Remote sensing in armed conflict studies: a bibliometric analysis Tangarife–Marulanda, Maritza Yulieth; Henao–Céspedes, Vladimir
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.v15i2.9995

Abstract

This article addresses the potential use of remote sensing in conducting studies related to armed conflict. To carry out this approach, a bibliometric analysis was carried out, applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, through which we sought to identify the most relevant authors, the geographical distribution of productivity, and the research trends in the area. The analysis was carried out with information obtained from the Scopus reference database in August 2024. The results indicate that productivity growth began in the 2000s and is still under development. Additionally, the review of the articles indicated that the use of remote sensing in relation to armed conflicts has focused mainly on the study of the effects of armed conflict on land cover, environmental stability and, more recently, on the impact on agricultural areas from a food security perspective.
Optimizing LTE-Advanced performance in urban networks using intra-band and inter-band carrier aggregation Putri, Hasanah; Anwar, Radial; 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.v15i2.10148

Abstract

The rapid increase in mobile data consumption has significantly impacted network performance, especially in densely populated urban areas. Long-term evolution (LTE)-Advanced, an enhancement of LTE under 3GPP Release 10, incorporates carrier aggregation (CA) to optimize spectral efficiency and data throughput. This study investigates the effectiveness of intra-band and inter-band CA methods in improving LTE-Advanced network performance in Cirangrang-Cibaduyut Kidul, a high-density urban area in Bandung, Indonesia. Using a combination of drive tests and network simulations in Atoll 3.3.0, Key performance indicators such as reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and throughput were analyzed. The results indicate that inter-band CA outperforms intra-band aggregation, providing significant improvements in signal quality and data rates. These findings suggest that inter-band CA is a viable solution for optimizing LTE-Advanced networks in urban environments with high traffic demand. This research provides practical recommendations for network operators to enhance service quality efficiently without extensive infrastructure expansion.
A techno-analytical insight on federated learning methodologies towards diagnosis of brain tumor Rangaswamyshetty, Kavitha Cholenahalli; Somashankaregowda, Maya Bevinahalli
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.v15i2.11055

Abstract

Artificial intelligence (AI) has made rapid progress in addressing complex medical challenges, including life-threatening conditions such as brain tumors. Recent years have witnessed significant contributions from machine learning (ML) and deep learning (DL), yet practical deployment remains limited due to privacy concerns, data heterogeneity, and lack of collaborative training. Federated learning (FL) offers a promising alternative by enabling distributed training across institutions without data sharing, thereby improving detection accuracy while preserving patient privacy. This paper systematically reviews FL in the context of brain tumor diagnosis, with a focus on its mathematical foundations, core modelling approaches, and emerging research trends. The analysis highlights that while FL demonstrates strong potential in enhancing classification, detection, and segmentation tasks, major gaps remain in handling non-independent and identically distributed (non-IID) data, cross-modal integration, scalability, and real-world deployment. The key insight of this study is that future progress will rely on hybrid FL systems, fairness-aware aggregation, and security-enhanced frameworks to achieve clinically viable, equitable, and scalable diagnostic solutions.
Fine-tuned LayoutLMv3 for Indonesian receipts extraction Sudana, Oka; Wirdiani, Ayu; Winama Putra, Andre Dwi
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.v15i2.10127

Abstract

Shopping is a transaction that generates a record as a payment receipt. Typically, a receipt is given as a small piece of paper that can be easily lost. It is essential to store the transaction information in the receipt digitally. Keeping the information in a digital form will make it easily accessible and will overcome the problem of easily lost receipts. Currently, the process of transferring receipt information into digital form is still being done manually. Having a system that can extract this information helps speed up the digitalization process tremendously. This research proposes a method that applies finetuning to the LayoutLMv3 model and with the help of optical character recognition (OCR) from Google Vision, can be used to extract transaction information contained in the receipt. The system works by using Google Vision to parse and segment every word contained within the receipt and its bounding box The LayoutLMv3 model will then assign labels to each word, and important words will be extracted. The finetuned LayoutLMv3 model successfully achieved an accuracy of 97.98% on training data and 90% accuracy on real-time test scenarios for extracting information on receipts written in the Indonesian.

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