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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 73 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 73 Documents clear
A metamaterial inspired multi band antenna using complementary split ring resonator for wireless applications Reddy, Harshavardhan; Patil, Rajendra R.
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9581

Abstract

This research introduces a new printed metamaterial antenna with triple and quad bands for wireless applications. The suggested antenna is constructed of FR4 material, with two slots created in the radiating element. In addition, a circular complementary split ring resonator (C-CSRR), is carved from the ground plane. HFSS simulation software is being put into use to design, model, and measure the suggested antenna parameters in a real-world environment. The measured results indicate that an antenna with C-CSRR behind the radiating patch resonates at three distinct frequencies, including 3.5 GHz, 7.5 GHz, and 8.2 GHz, and an antenna with C-CSRR and slots on the radiating patch resonates at four different frequencies, including 3.5 GHz, 7.5 GHz, 8.8 GHz, and 9.32 GHz. An antenna without complementary split ring resonator (CSRR), or a conventional antenna, resonates at 9.6 GHz. The metamaterial antenna results in a 65% diminution in antenna size in contrast to a regular microstrip antenna. The simulated outcome demonstrates that the suggested metamaterial antenna's peak gain is around 6 dB to 8 dB and it has a resonance frequency for C-band applications, including weather radar systems and 5G applications.
Prediction of postpartum depression in Zacatecas Mexico using a machine learning approach J. Ivan, Lopez-Veyna; Mariana, Ortiz-Garcia; Alvaro Moises, Diaz-Diaz; Carlos, Bermejo-Sabbagh
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9492

Abstract

Postpartum depression (PPD) is a silent disorder, difficult to detect by the mother who suffers from it. In this research project, we propose a classification model of PPD using machine learning (ML) techniques, following a supervised learning approach. This is model allows the prediction of PPD using sociodemographic and medical data through a dataset of 100 Zacatecan mothers previously classified with the result of Edinburgh Test. We use eight ML algorithms such as adaptative boosting classified (ABC), principal component analysis (PCA) boosting, decision trees (DT), k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), and boosting. Our results show that the proposed ML model based on ABC algorithm can outperform other classifiers yielding a precision of 90%, a recall of 90%, a F1-score of 78% and 74% for area under curve (AUC), illustrating a correct capability in the prediction of this disorder.
The effect of the number of NACA 4412 airfoil blades on the performance of a horizontal axis wind turbine Fauzan Adziimaa, Ahmad; Fazrin Widi Putri, Jasmine; Putu Eka Widya Pratama, I; Firyal Adila, Ahmad
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9632

Abstract

This work reports an experimental investigation of the effect of blade number on the performance of a small-scale horizontal-axis wind turbine (HAWT) using NACA 4412 airfoil blades. Two turbine prototypes (one with 7 blades and one with 9 blades) were fabricated and tested under controlled wind speeds (3.4–6.0 m/s). The turbine outputs were measured using INA219 current/voltage sensors and a TCRT5000 rotations per minute (RPM) sensor interfaced to an Arduino-based system for real-time data acquisition. Results show that the 9-blade turbine consistently generated higher electrical power and achieved a higher power coefficient than the 7-blade design. For example, at 3.4 m/s the 7-blade turbine produced about 0.0297 W versus 0.0471 W for the 9-blade turbine. The peak power coefficient reached ≈0.198 for the 9-blade rotor (vs. ≈0.195 for 7 blades) at the same wind speed. Sensor calibration indicated high accuracy (errors 1.2%), confirming the reliability of the measurements. These findings suggest that, for the tested design, increasing the number of blades improves small-HAWT performance. The developed wireless monitoring system and experimental results provide guidance for optimizing blade count in future small turbine designs.
An improved round robin time sharing algorithm for optimizing data mapping in cloud computing environments Abdelkader, Afaf; Mohamed, Asmaa; Ghazy, Nermeen
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.8087

Abstract

Cloud computing in recent years has been widely applied in a wide number of applications and fields. However, allocating tasks to virtual machines (VMs) remains a part that needs enhancement. Task scheduling algorithms in heterogeneous computing system are required to satisfy high-performance data mapping requirements. The efficient allocation between resources and tasks decreases waiting time (WT), turnaround time (TT) and maximizes resource utilization. Various task scheduling algorithms, including round robin (RR) and some improved RR algorithm are used for cloud environment. A novel time-sharing algorithm (NRRTSA) is introduced, demonstrating enhancements in WT and TT. Simulation findings indicate that the NRRTSA algorithm effectively schedules multiple requests (cloudlets) among several VMs, the proposed NRRTSA outperforms RR and other algorithms in terms of the average of both TT and WT. The average turnaround time (ATT) is enhanced with a ratio of 10.8% to 45%, the average waiting time (AWT) is enhanced with a ratio of 10.9% to 45%.
Replay attacks and sniffing in Bluetooth low energy communications with mobile phone Sebastian Orozco Duran, Juan; Paola Estupiñan Cuesta, Edith; Carlos Martínez Quintero, Juan
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10040

Abstract

This article analyzes vulnerabilities in Bluetooth low energy (BLE) connections in smartphones against replay and tracking attacks using software defined radio (SDR), evaluating four scenarios with BLE headsets and smartphones from different manufacturers through HackRF one, GNU radio, and Wireshark. In scenario 1, the advertising message ADV_NONCONN_IND was captured and retransmitted, generating persistent and deceptive pairing pop ups on smartphones. In scenario 2, fake pairing request signals were replicated to simulate a connection attempt, causing interface errors and deceptive notifications for the user. In scenario 3, complete pairing sequences were captured and replayed, producing false connection alerts and fabricated information such as battery level indicators from non existent devices. In scenario 4, passive tracking enabled the extraction of sensitive data during the pairing process, including ADV_IND packets, media access control (MAC) addresses, frequencies, manufacturer identifiers, and transmission power levels. A total of 93 successful and 123 failed attacks were recorded, with abnormal behaviors observed such as false pairing requests and manipulated device data, exposing users to risks of identity spoofing, denial of service (DoS) attacks, or targeted interference. The results highlight BLE protocol weaknesses against radio frequency (RF) based attacks and demonstrate the potential of SDR tools as powerful instruments for wireless protocol validation and cybersecurity research.
Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification Afuan, Lasmedi; Hidayat, Nurul; Permadi, Ipung; Iqbal, Iqbal; Suprihanto, Didit; Bintang Pradana Yosua, Panky; Alfarez Marchelian, Reyno
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9980

Abstract

Political ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (Bi-GRU) neural network models in classifying liberal and conservative political ideologies from social media text data. The Bi-GRU achieved the best results with 88.75% accuracy and 89.16% F1-score, highlighting its strength in contextual analysis. These findings suggest their applicability in areas such as election monitoring and the analysis of political discourse. This study contributes to the field of political text classification by offering a comparative analysis of deep learning architectures. The dataset utilized covers a wide range of issues, including social, political, economic, religious, and racial topics, demonstrating its comprehensive nature. Visualizations using WordCloud and uniform manifold approximation and projection (UMAP) reveal distinct ideological patterns, validating the dataset’s quality for training models. The findings underscore the importance of utilizing advanced bidirectional architectures for nuanced tasks, such as ideology classification, where contextual understanding is crucial. These insights open avenues for future research, such as the application of Bi-GRU in analyzing multilingual political ideologies or real-time sentiment tracking during election campaigns.
Research on optimal design of surface permanent magnet synchronous generator Hoai Nam, Phan; Duc Chuyen, Tran
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10305

Abstract

The more fossil energy is used, the less this energy source will become because it is not an infinite energy and it pollutes the environment, so there is a need for solutions with new and infinite energy sources such as wind energy. This paper designs and focuses on optimizing a floating magnet synchronous generator (SG) for a wind power generation system using finite element analysis (FEA) with ANSYS Maxwell software. This generator is compared with other types of generators such as squirrel cage induction generator (SCIG), wound rotor induction generator (WRIG), SG, doubly-fed induction generator (DFIG), and switched reluctance generator (SRG). Throughout the analysis and design process, the paper emphasizes the significant benefits of surface-mounted permanent magnet (SPM) motors in increasing efficiency and reliability while reducing supply costs. The research results of the paper aim to demonstrate that SPM can meet the needs of high efficiency and low cost in the industrial and civil fields. The results of this study by the authors will provide new contributions to serve as a basis for the design, manufacture, calculation and control of Halbach permanent magnet (Halbach PM) electric machines based on optimization techniques such as genetic algorithms (artificial intelligence) and sustainable optimization (for electrical equipment).
Memory faults using open and short defect models for nano technology applications Muddapu, Parvathi; Venkatesh, Maddela
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.6189

Abstract

As technology progresses from sub-micron to nanometer scales, memory-based systems are increasingly prone to faults. Consequently, developing robust methodologies to achieve defect-free embedded static random-access memory (SRAM) has become a critical challenge in modern very large scale integration (VLSI) design. Also, the increased integration of layout layers leads to form unknown defects. From the existing literature, observed that huge parametric variation is present whenever technology is changed. This is the key issue addressed in this paper, by representing an analysis on the impact of open and short defect models that uses parasitic extraction method while drawing various fault models. Possible open/short defects between the existing nodes are considered for the development of fault models using 45 nm, 32 nm, and 7 nm technologies. The total number of fault models of both kinds observed are 147. Also observed that besides to the existing faults, few undetectable faults are found named as undefined short faults (USF), undefined write after read fault (UWARF), and few faults with multiple faulty behavior.
Autism detection using facial and motor analysis using machine learning Amirbay, Aizat; Baigabylov, Nurlan; Mukhanova, Ayagoz; Mukhambetova, Kuralay; Zaitov, Elyor; Burganova, Roza; Khusanova, Khayriniso; Akhmedova, Feruza
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10319

Abstract

This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
Multi-feature fusion framework for enhanced image deduplication accuracy using adaptive deep learning Shah, Rahul; Kumar Shrivastava, Ashok
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9119

Abstract

Image deduplication is a critical task in domains such as digital asset management, content-based image retrieval (CBIR), and data storage optimization. This paper presents a novel method for improving deduplication accuracy by integrating multiple feature types. A comprehensive framework is proposed that combines visual, semantic, and structural image elements. The system employs deep learning architectures, including convolutional neural networks (CNNs) and transformers, to extract high-level features, which are fused through an adaptive weighting mechanism that dynamically adjusts based on image content. Experimental results across diverse datasets demonstrate that the proposed multi-feature fusion approach significantly outperforms traditional single-feature methods, achieving an average improvement of 15% in deduplication accuracy. By overcoming limitations in handling complex visual similarities, this study introduces a more robust and efficient solution for image deduplication.

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