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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 2,901 Documents
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The effect of feature selection with optimization on taxi fare prediction A. Naim, Amany; Hekal Omar, Asmaa; A. Ibrahim, Asmaa; Mohamed, Asmaa; M. Mostafa, Naglaa
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8658

Abstract

Feature selection plays a key influence in machine learning (ML); the main objective of feature selection is to eliminate irrelevant and redundant variables in different classification problems to improve the performance of the learning algorithms. Classification accuracy is improved by reducing the number of selected features. Many real-world problems, such as taxi fare can be predicted by ML. This paper proposes feature selection using genetic algorithm (GA) optimization to predict taxi fare. Experiments are performed on real datasets of taxi fare, and this paper uses eight classifiers to evaluate the selected features. The performance of the classifiers is assessed using various performance metrics. The results are compared with feature selection without optimization. The proposed method records high classification accuracy when evaluated by three types of classifiers (random forest, AdaBoost, and Gradient Boost). The results indicate that the prediction accuracy of the proposed method is 99.7% on taxi fare dataset.
Compatibility of transformer construction materials with mineral, natural ester, and synthetic ester insulating oils Sutan Chairul, Imran; Hidayah Rahim, Nor; Ab Ghani, Sharin; Shahril Ahmad Khiar, Mohd; Syahrani Johal, Muhammad; Nazri Mohamad Din, Mohamad
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8391

Abstract

This paper presents the experimental findings on the compatibility of kraft paper strips and gasket materials (fluoroelastomer (FE) and nitrile butadiene rubber (NBR)) with mineral (MO), natural ester (NE), and synthetic ester (SE) insulating oils. First, three insulating oil samples were prepared, and kraft paper strips, pressboards, and FE and NBR gasket materials were immersed in the oils. Metal catalysts were added into the insulating oil samples to simulate the actual conditions of oil-immersed transformers. The samples were thermally aged at 130 °C for 400 h. The results show that the tensile strength of the kraft paper immersed in NE increased by 1.82%, while the tensile strength of the kraft paper immersed in MO and SE decreased by 6.23 and 0.80%, respectively. The Shore A hardness of FE thermally aged in MO and SE decreased by 2.64 and 11.16%, respectively. In contrast, the FE thermally aged in NE became slightly harder, with a percentage degradation of +1.62%. On the other hand, the NBR thermally aged in MO, NE, and SE drastically decreased by 94.30, 86.70, and 93.67%, respectively. Hence, it is concluded that NBR is incompatible with the insulating oils tested in this study. In contrast, FE is most compatible with NE, followed by MO and SE.
Development of frequency modulated continuous wave radar antenna to detect palm fruit ripeness Rahmawati, Yosy; Rizkinia, Mia; Zulkifli, Fitri Yuli
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8749

Abstract

Oil palm fruits farmers in Indonesia have determined the ripeness of oil palm fruits in the traditional way, namely using human eye visuals, which have the weakness of inconsistent levels of accuracy and are prone to errors. The development of increasingly sophisticated technology will help oil palm fruits farmers recognize the characteristics of fruit maturity. Advanced technology, such as frequency modulated continuous wave (FMCW) radar, can assist farmers in accurately identifying fruit maturity. To ensure high accuracy and sensitivity, an antenna with low side lobe level (SLL), high gain, and wide bandwidth in the 23-26 GHz range is required. Using CST Microwave Studio 2023, a designed and simulated antenna achieved an SLL of 24 dB, a gain of 15 dBi, and a bandwidth of 2.5 GHz. These results indicate that higher gain enhances energy directionality and overall antenna performance. Additionally, a smaller angular value improves the antenna’s radiation focus, making it more effective for precision sensing in oil palm fruit ripeness detection.
Chili leaf segmentation using meta-learning for improved model accuracy Suwarningsih, Wiwin; Kirana, Rinda; Husnul Khotimah, Purnomo; Riswantini, Dianadewi; Fachrur Rozie, Andri; Nugraheni, Ekasari; Munandar, Devi; Arisal, Andria; Roufiq Ahmadi, Noor
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7929

Abstract

Recognizing chili plant varieties through chili leaf image samples automatically at low costs represents an intriguing area of study. While maintaining and protecting the quality of chili plants is a priority, classifying leaf images captured randomly requires considerable effort. The quality of the captured leaf images significantly impacts the development of the model. This study applies a meta-learning approach to chili leaf image data, creating a dataset and classifying leaf images captured using mobile devices with varying camera specifications. The images were organized into 14 experimental groups to assess accuracy. The approach included 2-way and 3-way classification tasks, with 3-shot, 5-shot, and 10-shot learning scenarios, to analyze the influence of various chili leaf image factors and optimize the classification and segmentation model's accuracy. The findings demonstrate that a minimum of 10 shots from the meta-test dataset is sufficient to achieve an accuracy of 84.87% using 2-way classification meta-learning combined with the mix-up augmentation technique.
Enhanced real-time glaucoma diagnosis: dual deep learning approach Hesham, Mai; Kareem, Ghada; Hadhoud, Marwa
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8495

Abstract

Effective management of glaucoma is essential for preventing irreversible vision loss. This study introduces a novel deep learning-based network designed to enhance performance while minimizing computational complexity. The system comprises two models: the first is a hybrid model combining a customized U-Net architecture integrated with you only look at coefficients (YOLACT) is utilized to achieve accurate segmentation of the optic disc (OD) and optic cup (OC), providing detailed diagnostic insights for ophthalmologists. The second model employs you only look once version 5 (YOLOv5) for real-time glaucoma prediction, delivering outstanding performance with an accuracy of 97.89% and F1 score of 98% on the primary dataset. On an independent dataset without further training, the model achieved 96% accuracy, with sensitivity and specificity of 98.9% and 93.3%, respectively. These results highlight the model's robustness, generalizability, and adaptability, demonstrating its potential for effective glaucoma screening and early detection in diverse clinical environments. This approach offers a promising advancement in improving the accessibility and efficiency of glaucoma management.
Recommender systems in real estate: a systematic review Henríquez-Miranda, Carlos; Ríos-Pérez, Jesús; Sanchez-Torres, Germán
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8884

Abstract

The constant growth of online real estate information has emphasized the need for the creation and improvement of intelligent recommendation systems to help mitigate the difficulties associated with user decision-making. This systematic review, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and criteria, investigates current approaches and models used in real estate recommendation systems, with a focus on papers published in 2019 and 2024. The review identifies four main techniques: content-based filtering, collaborative filtering, knowledge-based systems, and hybrid approaches. Key findings indicate a preference for deep learning models, specifically convolutional neural network and long-short term memory (CNN-LSTM) architectures, and highlight the most used property characteristics: price, number of rooms, size, and location. The research addresses several important challenges, including the cold start problem, data sparsity, and the importance of adaptive learning in dynamic markets. Potential future research fields are outlined, with a focus on hybrid model architectures, attention mechanisms, and explainable artificial intelligence (AI). This review provides a comprehensive overview of the field, enabling scholars and practitioners to improve the accuracy and user experience of real estate recommendation systems.
Linear algorithm for data retrieval performance optimization in self-encryption hybrid data centers M. Al Assaf, Maen; Qatawneh, Mohammad; AlRadhi, AlaaAldin
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9320

Abstract

Contemporary data centers implement hybrid storage systems that consist of layers from solid-state drives (SSDs) and hard disk drives (HDDs). Due to their high data retrieval speed, SSDs layer is used to store important data blocks that have features like high frequency of access. To boost their security level, many of such systems implement self-encryption algorithms like advanced encryption standard (AES), Blowfish, and triple data encryption standard (3DES) with different key sizes that vary in their complexity and their decryption latency whenever a block is requested for read. Frequently accessed data blocks with increased decryption latencies are better to be migrated to the SSDs layer to decrease their retrieval latency. In this paper, we introduce a linear complexity algorithm hybrid self-encryption storage data migration (HSESM) that migrates important data blocks that requires long decryption latencies from the HDDs layer to the SSDs one. Performance evaluation shows that HSESM data migration process can reduce data blocks read latencies in 13.71%-23.61% under worst-case scenarios.
Ensemble and deep learning via median method for learning disability classification P. J., Anu; Ranjith Singh, K.
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8639

Abstract

The study explores the classification of students with and without learning disabilities (LD) through machine learning techniques, utilizing a real dataset and implementing bootstrapping for data augmentation. Noteworthy findings reveal the Adam optimizer's superior performance among various optimizers, achieving a true positive rate (TPR) of 0.97 and a false positive rate (FPR) of 0.02, with high precision, recall, and f1-score values. Additionally, ensemble learning, employing the median method, combines models like Random-ForestClassifier and KerasClassifier, and BaggingClassifier with KerasClassifier, resulting in improved performance. However, the Median-Combined model, integrating AdaBoostClassifier and KerasClassifier, stands out with an accuracy of 99.6%, along with elevated precision, recall, and f1-score values. The comprehensive classification report showcases an overall FPR of 0.0 and TPR of 0.999, highlighting the enhanced performance of the combined model. The significance of this study lies in underscoring the power of fusion between ensemble learning and deep learning techniques, leveraging the median method. This combined model exhibits superior performance, excelling in accuracy, precision, recall, and overall classification effectiveness. The innovative approach of combining both ensemble and deep learning methods through the median method not only advances the understanding of learning disability classification but also emphasizes the practical importance of integrating diverse methodologies for enhanced model performance.
Event-driven integration of electronic medical records with blockchain and InterPlanetary file system Arissabarno, Cahyo; Sukaridhoto, Sritrusta; Winarno, Idris; Putri Nourma Budiarti, Rizqi
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9355

Abstract

The integrity, security, and accessibility of electronic medical record (EMR) are often compromised by traditional systems, which struggle to ensure data integrity, transparent audit trails, and secure long-term storage. This research addresses these challenges by integrating EMR with a private blockchain and InterPlanetary file system (IPFS) cluster, using change data capture (CDC) for real-time updates and integrate with existing EMR systems, avoiding the need for building new EMR software. Implemented in the OpenEMR framework, the system's performance is evaluated across various processes, including document uploading, sharing, access, deletion, and integrity verification. Testing with anonymized medical records in PDF formats ranging from 1 MB to 100 MB shows that uploading to IPFS takes 0.7 seconds per MB, blockchain transaction processing averages 4.2 seconds, CDC time is 1.1 seconds per MB, and OpenEMR uploads average 0.98 seconds per MB. These results demonstrate significant improvements in data security, integrity, and availability, following the CIA triad principles. The system provides a traceable and secure solution for EMR management.
Enhancing low-light pedestrian detection: convolutional neural network and YOLOv8 integration with automated dataset Rendi, Rendi; Fitrianah, Devi
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8903

Abstract

This research aims to enhance the you only look once (YOLO) model for pedestrian detection in environments with varying lighting conditions, particularly in low-light scenarios. The primary contribution of this work is the integration of a convolutional neural network (CNN)-based low-light enhancement model, which transforms dark images into brighter, more discernible ones. This enhanced dataset is subsequently used to train the YOLO model, allowing it to learn from both the original and transformed data distributions. Unlike traditional YOLO training approaches, this method generates more accurate data representations in challenging lighting environments, leading to improved detection outcomes. The novelty of this approach lies in its dual-stage training process, which integrates a CNNbased low-light enhancement model with YOLO’s detection capabilities. This combination not only enhances pedestrian detection but also has the potential for application in other domains, such as vehicle detection and surveillance, particularly in challenging lighting conditions. The automatic dataset collection pipeline provides an efficient way to gather diverse training data across various scenarios. The YOLOv8 model trained on the low-light enhanced dataset significantly outperformed the baseline model trained only on the original dataset, with precision increased by 9.8%, recall by 45.7%, mAP50 by 26.8%, and mAP50-95 by 41.0% when validated on dark images.

Page 258 of 291 | Total Record : 2901

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