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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 73 Documents
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Search results for , issue "Vol 14, No 4: August 2025" : 73 Documents clear
Residual pixel-wise semantic segmentation for assessing enlarged fetal heart: a preliminary study Roseno, Muhammad Taufik; Nurmaini, Siti; Rini, Dian Palupi; Saputra, Tommy; Mirani, Putri; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Syaputra, Hadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9244

Abstract

The four-chamber view is a crucial scan plane routinely employed in both second-trimester perinatal screening and fetal echocardiographic examinations. Sonographers typically measure biometrics in this plane, such as the cardiothoracic ratio (CTR) and heart axis, to diagnose fetal heart anomalies. However, due to the echocardiographic artifacts, the assessment not only suffers from low efficiency but also inconsistent results depending on the operators’ skills. This study proposes a residual pixel-wise semantic segmentation, which segmented the fetal heart and thoracic contours in a 4-chamber view for assessing an enlarged fetal heart condition. The accuracy of intersection-over-union (IoU) and dice coefficient similarity (DCS) is used for model validation to further regulate the evaluation procedure. We use 1174 US images, comprising about 560 enlarged heart images, and about 614 normal heart images. Out of these data, 248 images are used for unseen data, and the remaining for training/validation processes. The performance of the proposed model, when tested on unseen data, achieved satisfactory results with 97.71% accuracy, 90.36% IoU, and 94.93% DCS. These metrics collectively demonstrate the satisfactory performance of the proposed model compared to existing segmentation models. The outcomes underscore that the proposed model establishes a state-of-the-art standard for enlarged fetal heart detection.
Adaptive fuzzy sliding-mode control for robot manipulator with uncertain model and external disturbance Dinh Co, Hoang; Cuong, Nguyen Cao
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9487

Abstract

In practice, robots operate as nonlinear systems and often encounter factors like nonlinear friction, load variations, and external disturbances during tasks. To address these challenges, a smart control approach has been developed that combines the strengths of fuzzy logic and sliding mode control (SMC) for precise robot manipulator positioning. The key benefit of SMC lies in its robustness, maintaining stability despite noise or parameter changes in the system. However, designing an SMC system often faces difficulties due to practical limitations, making deployment not always feasible in real-world applications. Additionally, a large control law amplitude can lead to chattering around the sliding surface. To overcome these issues, the study introduces a fuzzy logic-based method to adaptively estimate the control law's magnitude, guided by Lyapunov stability principles. This control scheme is tested on a four-degree-of-freedom robot manipulator, with simulation results confirming its effectiveness in MATLAB.
Metaheuristic algorithm for optimal allocation of electric vehicles and photovoltaics in distribution grid Kallangad Madhavan, Kavitha; Subbaraya Raviprakasha, Magge; Lakshmegowda Suresh, Haleyur
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9550

Abstract

Since electric vehicles (EVs) emit less carbon dioxide, their number is rapidly increasing. As the number of EVs grow, these added loads strain the distribution grid, introducing new challenges. Key concerns for network operators include voltage fluctuations and increased power losses. Properly deploying throughout the grid, photovoltaic (PV) systems and electric vehicle charging stations (EVCS) can assist in lowering power losses and improving the bus voltage profile. A MATLAB implementation of the metaheuristic algorithm called Harris Hawk optimization (HHO) algorithm is developed to select the best locations for integrating EVCSs and PVs, with the goals of enhancing the voltage profile and reducing power losses across buses. IEEE 12-bus and 14-bus systems and real-time distribution grid data were used to test the method. For the 26-bus real-time system, the results demonstrated a notable 24% decrease in overall power loss as compared to the base case and improved voltage regulation, as indicated by a lower average voltage deviation index (AVDI) value of 0.0929. A comparative analysis was performed between optimized and random placements of EVCSs and PVs, as well as against the grey wolf optimization (GWO) algorithm. The results provide a framework for implementing solar-powered EV charging infrastructure. This can reduce costs, enhance energy reliability, and contribute to a cleaner environment.
Exploring deep learning approaches for image captioning to mimic human understanding Islam, Maheen; Hassan Ratul, Mahedi; Haque, Rezaul; Hossain Rony, Sazzad; Huq Asif, Azharul; Mittra, Tanni; Miskat Hossain, Md; Hasan, Mahamudul
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8885

Abstract

Image captioning has emerged as a vital research area in computer vision, aiming to enhance how humans interact with visual content. While progress has been made, challenges like improving caption diversity and accuracy remain. This study proposes transfer learning models and RNN algorithms trained on the microsoft common objects in context (MS COCO) dataset to improve image captioning quality. The models combine image and text features, utilizing ResNet50, VGG16, and InceptionV3 with LSTM, and BiLSTM. Performance is measured using metrics such as BLEU, ROUGE, and METEOR for greedy and beam search. The InceptionV3+BiLSTM model outperformed others, achieving a BLEUscore of over 60%, a METEORscore of 28.6%, and a ROUGEscore of 57.2%. This research contributes to building a simple yet effective image captioning model, providing accurate descriptions with human-like understanding. The error was analyzed to improve results while discussing ongoing research aimed at enhancing the diversity, fluency, and accuracy of generated captions, with significant implications for improving the accessibility and searchability of visual media and informing future research in this area.
Enhanced detection of android ransomware families using machine learning and network traffic analysis Singh, Manmeet Mahinderjit; Selvaraj, Kalaivani; Wei, Zhao
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9485

Abstract

Ransomware attacks on Android devices often go undetected until damage occurs, as prevention strategies are limited by inconsistent threat detection and classification. This paper presents a framework for evaluating machine learning models to detect and classify Android ransomware families through network behavioral analysis. The framework extracts discriminative features from network traffic data and segregates them into four optimal clusters using the k-means clustering method. A total of 84 critical network traffic features are identified, including source IP, destination IP, source port, destination port, traffic duration, and the total number of forward and reverse packets. These optimal features are effectively utilized to train well-known machine learning models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector machines (SVM), and bagging, to evaluate their accuracy in classifying ransomware families. Simulation results demonstrate that RF achieves the best performance with an accuracy of 95.18%, precision of 95.21%, recall of 95.27%, and F1-score of 95.19%. This framework, focused on network behavioral analysis rather than static or dynamic analysis, provides deeper insights into the behavior and characteristics of ransomware.
Design of internet of things-integrated programmable logic controller for demonstrating automated sorting systems Intawong, Narit; Saengchandr, Banjerd; Inkamchuer, Manit; Thongprom, Morakot; Sukontanakarn, Viroch
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9426

Abstract

This project presents an automated workpiece sorting demonstration system controlled by a programmable logic controller (PLC) and a touch screen interface. The system integrates an internet of things (IoT) gateway that communicates with the PLC via Modbus remote terminal unit (RTU) over RS-485, allowing the transfer of digital data. This data is processed using JavaScript within the Node-RED platform to manage machine operations and display the operational status. The system supports both manual control and IoT-based management, enabling the sorting of cylindrical workpieces to designated areas. Metal and non-metal detection is achieved using capacitive and inductive sensors, respectively, which inform a stepper motor to manipulate the workpieces via a gripper pneumatic to the specified locations. Test results indicate a high detection capability of the sensors: the capacitive sensors achieved a 95% detection rate over 100 trials, while the inductive sensors recorded a 97% detection rate. Furthermore, the precision of placing workpieces at the target locations was 92% across 100 attempts. This system showcases an effective combination of automation and IoT technologies, improving efficiency in workpiece sorting processes.
A machine and DL approach for classifying customer sentiments from online shopping reviews in Bangla text Arifur Rahman Rejuan, Md.; Assaduzzaman, Md; Fahad, Nafiz; Jakir Hossen, Md.; Rahmatul Kabir Rasel Sarker, Md.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9122

Abstract

Due to the widespread availability of the internet all across the world, people prefer shopping online rather than going to a shop. There are various online marketplaces available in Bangladesh, like Daraz, Pickaboo, Rokomari, Othoba, Bikroy, Food Panda, and Robi Shop. With the increasing quantity of customers on online shopping platforms, the number of product reviews also increases with it. Data is classified utilizing machine learning (ML), deep learning (DL), transfer learning, and other data mining algorithms to facilitate the customer’s comprehension of the primary subject of the review before making a purchase. Natural language processing techniques are employed to categorize data in any given language for such issues. There are no Bengali shopping review datasets available on online sites. So, we manually collected a dataset of 2,600 reviews. In this paper, reviews are classified into 5 categories (satisfied, very satisfied, not satisfied, fairly satisfied, and satisfied but delivery problem). DL (long short-term memory (LSTM) and convolutional neural network (CNN)) and ML (support vector machine (SVM), random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost)) model have been applied. Among the DL models, CNN has the best accuracy (91.27%), and the RF classifier provides the highest accuracy (84.39%) out of all the ML models.
Optimized indoor radio signal prediction with 3D ray tracing model at 2.4 and 5 GHz Tarapiah, Saed; Sulaiman, Batoul; Natsheh, Emad; Atalla, Shadi; Abu Kharmeh, Suleiman; Rashed, Abdallah
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9441

Abstract

Channel propagation models are essential in developing efficient wireless communication networks. Indoor propagation relies on the nature of the surrounding environment. Therefore, many researchers have provided different ways for effective propagation modeling and received power prediction. In this paper, ray-tracing-based site-specific propagation models are presented. The actual measurements are obtained using many wireless access points (AP) based on IEEE 802.11 with different technologies a/b/g and n as transmitters and mobile phone with a proposed mobile application used as a receiver to collect the power at different locations called reference points (RPs), these measurements are done without the existence of people movement. The simulation results are obtained using wireless InSite simulator depends on 3D shoot and bounce ray (SBR) method. The simulation measurements are assessed by comparing it with the actual measurements and they analyze statistically such that the correlation coefficient R between them reaches up to 80% which is an indicator to an acceptable agreement. Path loss characteristic affected by the building materials and distance along the receiver’s route is evaluated.
Performance comparison of algorithms in the classification of fresh fruit types based on MQ array sensor data Hananto, Bayu; Raafi'udin, Ridwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9070

Abstract

Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring quality control, minimizing waste, and enhancing food safety across the supply chain. This study evaluates the performance of four machine learning algorithms—artificial neural network (ANN), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF)—in classifying fruit freshness based on data obtained from electronic noses equipped with MQ array sensors. Experiments were conducted using a comprehensive dataset comprising various fruit combinations, and model performance was assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that the RF algorithm achieved the highest accuracy (100%) and precision (1.00), demonstrating superior performance in both classification accuracy and computational efficiency. ANN and KNN also performed well, with accuracies of 96.80% and 97.10%, respectively, while LR yielded a lower but still effective accuracy of 91.16%. Statistical analysis confirms that RF's superior performance is statistically significant when compared to the other algorithms. These findings suggest that RF is the most effective algorithm for fruit freshness classification using electronic nose data, offering fast and reliable results that are well-suited for integration into real-time monitoring systems in agricultural and food retail applications.
Development of classification model for thoracic diseases with chest X-ray images using deep convolutional neural network Okokpujie, Kennedy; Anointing, Tamunowunari-Tasker; Ijeh, Adaora Princess; Okokpujie, Imhade Princess; Ogundele, Mary Oluwafeyisayo; Oguntuyo, Oluwadamilola
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9300

Abstract

Thoracic disease is a medical condition in the chest wall region. Accurate thoracic disease diagnosis in patients is critical for effective treatment. Atelectasis, mass, pneumonia, and pneumothorax are thoracic diseases that can lead to life-threatening conditions if not detected and treated early enough. When diagnosing these diseases, human expertise can also be susceptible to errors due to fatigue or emotional factors. This research proposes developing a real-time deep learning-based classification model for thoracic diseases. Three deep convolutional neural network (CNN) models - MobileNetV3Large, ResNet-50, and EfficientNetB7 - were evaluated for classifying thoracic diseases from chest X-ray images. The models were tested in 5-class (atelectasis, mass, pneumothorax, pneumonia, and normal), 4-class (atelectasis, pneumothorax, pneumonia, and normal), and 3-class (atelectasis, pneumonia, and normal) modes to assess the impact of high interclass similarity. Retrained MobileNetV3Large achieved the highest classification accuracy: 75.72% next to ResNet-50 (75.2%) and last EfficientNetB7 (73.03%). For the 4-class, EfficientNetB7 (88.08%) led with MobileNetV3Large in the last (87.08%), but MobileNetV3Large led the 3-way with 97.88% with EfficientNetB7 again in the last (96.55%). These results indicate that MobileNetV3 can effectively distinguish and diagnose thoracic diseases from chest X-rays, even with interclass similarity and supports the use of computer-aided detection systems in thoracic disease classification.

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