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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.
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Articles 75 Documents
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Search results for , issue "Vol 14, No 3: June 2025" : 75 Documents clear
Design and performance evaluation of a high-efficiency circular microstrip patch antenna for RFID applications at 900 MHz Sahel, Zahra; Habibi, Sanae; Bendali, Abdelhak; ALtalqi, Fatehi; Mouhib, Omar; Habibi, Mohamed
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.v14i3.9110

Abstract

This study presents a high-efficiency circular microstrip patch antenna designed for radio frequency identification (RFID) applications simulation results illustrate the performance of a circular microstrip patch antenna operating at 900 MHz. Microstrip antennas are renowned for their ability to meet the requirements of compact, lightweight designs, ensuring compatibility, and ease of integration. This research focuses on the development of a circular microstrip antenna, formed as a circular patch on a 0.035 mm thick FR-4 substrate. The design was realized using a substrate with a relative permittivity (εr) of 4.3, a loss tangent (tan δ) of 0.021 and a substrate height (h) of 1.6 mm. The antenna dimensions are small, measuring 58×45 mm, with a circular patch radius of 17 mm. The antenna operates over a frequency range from 0.5 GHz to 2 GHz. Key performance parameters include a return loss of -49.8 dB, a wide bandwidth of 150 MHz, a voltage standing wave ratio (VSWR) of 1.009, a gain of 2.161 dB, and a directivity of 2.200 dBi. Antenna design and simulation were carried out using computer simulation technology (CST) Studio Suite Software, specifically adapted to RFID applications.
Driving behavior analytics: an intelligent system based on machine learning and data mining techniques Arabiat, Areen; Altayeb, Muneera
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.v14i3.9095

Abstract

One of the most common causes of road accidents is driver behavior. To reduce abnormal driver behavior, it must be detected early on. Previous research has demonstrated that behavioral and physiological indicators affect drivers' performance. The goal of this study is to consider the feasibility of classifying driver behavior as either aggressive (sudden left or right turns, accelerating and braking), normal (average driving events) or slow (keeping a lower-than-average speed). Innovation in data mining and machine learning (ML) has allowed for the creation of powerful prediction tools. ML techniques have shown potential in predicting driver behavior, with classification being a critical study area. The data set was gathered using the Kaggle platform. This study classifies driver behavior using Orange3 data mining tools and tests several classifiers, including AdaBoost, CN2 rule inducer, and random forest (RF) classifiers. The results showed that AdaBoost was superior in predicting driver behavior, with 100% accuracy, while the classification accuracy in CN2 rule inducer and RF was 99.8% and 95.4%, respectively. These results demonstrate the possibility of early and highly accurate driver behavior prediction and use it to create a ML-based driver behavior detection system.
Deep residual bidirectional long short-term memory fusion: achieving superior accuracy in facial emotion recognition Munsarif, Muhammad; Ku-Mahamud, Ku Ruhana
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.v14i3.9090

Abstract

Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn-Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning Umamaheswari, Umamaheswari; Kannan, Kannan; Rozario, Juliet; Manimekala, Manimekala
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.v14i3.9400

Abstract

The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bioinformatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification.
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.

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