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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 14, No 6: December 2025" : 75 Documents clear
3D mapping for unmanned aerial vehicle combining LiDAR and depth camera in indoor environments Tran, Hoang Thuan; Vo, Chi Thanh; Ha, My Duyen; Tu, Nong Trong; Ngan, Du Van; Le, Nam Hoai; Hoa, Duong Van
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10573

Abstract

Indoor reconnaissance missions for unmanned aerial vehicles (UAVs) pose significant challenges in scene reconstruction, mapping, and environmental feature extraction. Relying on a single type of sensor often results in limited accuracy, increased susceptibility to environmental noise, and a lack of comprehensive spatial information. To address these issues, this study proposes a mapping method that combines light detection and ranging (LiDAR) and depth camera data. The method collects data from both LiDAR and a depth camera integrated on the UAV, then performs preprocessing on both data sources to construct local 3D maps using the real-time appearance-based mapping (RTAB-Map) algorithm. Subsequently, the local maps are merged using a filtering method to generate a detailed and complete global map. Real-time experiments conducted on Ubuntu 20.04 using the robot operating system (ROS) Noetic libraries demonstrate that this multi-sensor fusion approach provides richer and more comprehensive environmental information, thereby enhancing the effectiveness of mapping tasks in unknown indoor environments.
Dispersion compensation in single and multi-channel DWDM using chirped apodized fiber Bragg gratings Kalkala Balakrishna, Kripa; Murthy Chinnammahalli Ramappa, Gopalakrishna; Palani, Karthik
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10599

Abstract

Chromatic dispersion (CD) is a key limiting factor in long-haul optical fiber communication, particularly in multi-channel dense wavelength division multiplexing (DWDM) systems, where it introduces signal distortion and inter-symbol interference (ISI). This paper proposes a low-dispersion-offset compensation (LDOC) scheme employing Gaussian-apodized linear chirped fiber Bragg gratings (CFBGs) to enhance dispersion management in single and multi-channel DWDM optical fiber communication systems. Simulations were performed in OptiSystem 7.0 for 10 Gbps single-channel transmission over standard single-mode fiber (SSMF) spanning 110–210 km, and were extended to 4- and 8-channel DWDM systems with a 0.8 nm channel spacing. System performance was evaluated in terms of quality factor (Q-factor), bit error rate (BER), and eye height under varying fiber lengths, input powers, and chirp coefficients. The LDOC-enhanced CFBG achieved a Q-factor of 7.04 with a BER of 9.82×10⁻¹³ for single-channel transmission at 180 km, 13.83 with a BER of 5.57×10⁻⁴¹ for a 4-channel system at 150 km, and 7.56 with a BER of 7.76×10⁻¹¹ for an 8-channel system at 150 km. These results confirm significant improvements compared to conventional CFBGs, demonstrating that the proposed LDOC-based approach is a compact and effective solution for next-generation metro-core, long-haul, DWDM, and 5G/6G optical networks.
Modeling the process of magma rising in the bowels of the Earth and its eruption to the surface Taurbekova, Ainur; Karimsakova, Balnur; Mamyrbayev, Orken; Toigozhinovа, Ainur; Tergeusizova, Aliya; Doshtaev, Kuntugan; Aidaraliyeva, Zarina; Utepova, Elvira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10454

Abstract

This paper presents a numerical model for simulating magma ascent in the Earth’s interior and its eruption to the surface, aimed at improving earthquake prediction. Magmatic flows are modeled as highly viscous fluids with a low Reynolds number (Re) using simplified Navier–Stokes equations. The approach incorporates hydrodynamic instability arising from density differences between magmatic and asthenospheric layers. Initial and boundary conditions were formulated for magma outflow from a narrow crack, and a dimensionless Euler–Reynolds (ER) parameter was introduced to characterize flow behavior. Numerical experiments for different ER values revealed that at low ER, magma spreads slowly, forming stable layers, while higher ER values accelerate vertical rise, increase pressure gradients, and enhance instability. The model identifies zones of stress accumulation that may precede seismic events. An additional method—monitoring fluid levels in deep wells—showed correlation with seismic fluctuations, supporting its potential for early warning. The results confirm the reliability of the proposed approach, demonstrating good agreement with seismological data. The developed methodology can be applied to enhance early warning systems and reduce risks in seismically active regions.
Design of compact dual-resonance multiple-input-multiple-output antenna array for internet of things application Kadu, Mahesh; Chitte, Pankaj Pramod; Udawant, Sandip R.; Ubale, Vilas S.
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10035

Abstract

This work presents a compact dual-resonance multiple-input-multiple-output (MIMO) antenna array for internet of things (IoT)-enabled smart devices requiring both 5G and Wi-Fi connectivity. The antenna operates at 3.6 GHz (5G smartphones) and 5.4 GHz (high-speed Wi-Fi), using a dual-resonance MIMO configuration for reliable multi-device communication. An integrated electromagnetic band-gap (EBG) structure suppresses surface waves, reducing mutual coupling and achieving 35 dB isolation with an envelope correlation coefficient (ECC) of 0.05. A fabricated prototype validated the design, with measurements aligning closely with simulations. The proposed antenna offers compactness, dual-band operation, low coupling, and strong MIMO performance, making it well-suited for next-generation IoT systems.
A powerful machine learning method for detecting phishing threats Baklizi, Mahmoud; Zraqou, Jamal; Alkhazaleh, Mohammad; Atoum, Issa; Alzyoud, Faisal; B. Alzghoul, Musab
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9579

Abstract

Phishing threats exploit social engineering and deceptive web infrastructure to steal sensitive personal information, often by mimicking legitimate websites. With the proliferation of online services and the increasing prevalence of cybercrime, detecting phishing websites has become a critical challenge. This study presents a comprehensive machine learning (ML)-based approach for detecting phishing websites. A total of 48 discriminative features were extracted from 10,000 web pages—comprising 5,000 phishing and 5,000 legitimate sites. Nine ML classifiers were initially evaluated, including random forest (RF), support vector machine (SVM), and XGBoost. Ensemble models based on soft voting and stacking were then constructed to improve detection performance. Among the models, the soft voting classifier (VC) achieved the best performance with an accuracy and F1-score of 98.82%. The results indicate that ensemble learning offers a robust solution for the automated detection of phishing websites.
Real-time browser-integrated phishing uniform resource locator detection via deep learning and fuzzy matching Linh, Dam Minh; Chau, Han Minh; Thua, Huynh Trong; Hung, Tran Cong
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10099

Abstract

Phishing attacks through deceptive URLs remain a critical cybersecurity threat, particularly in financial transactions and online payment systems. This study evaluates multiple deep learning (DL) models on the PhiUSIIL dataset of 235,795 URLs, with bidirectional gated recurrent unit (BiGRU) achieving the best performance—99.82% accuracy at a 60:40 split, along with high precision, F1-score, and the lowest test loss. To further improve detection of obfuscated URLs, an enhanced BiGRU variant is proposed using an expanded 366-character vocabulary. For real-time deployment, a Chrome extension is developed, integrating exact and fuzzy matching via the Ratcliff–Obershelp algorithm with cloud-based whitelist and blacklist checks. When fuzzy matching is inconclusive, the BiGRU model performs the final classification. By combining an adaptive browser-side tool with a robust DL backend, the proposed system ensures high accuracy, scalability, and efficiency for phishing detection in practical web environments.
On-edge 2D-to-3D generative pipeline for seamless instance transformation Petchhan, Jirayu; Doungtap, Surasachai
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10810

Abstract

Despite ongoing challenges with fragmented workflows, latency in device imports, and the main issue of limitations in object reconstruction functionality, relying on imperfect extraction networks remains an impractical solution for scalable object generation. To deal with these constraints, we proposed an end-to-end pipeline that leverages a re-designed self-consistency mechanism—aimed at reducing discrimination, along with the beneficial enhancement from level-set projection and gradient-surface orthogonality. In addition, our approach designs dynamic 3D object creation with minimal manual effort by unifying surface topology and optimizing data loading, enabling a streamlined reconstruction process and more flexible object projection. Our method supports rapid, resource-efficient mesh reconstruction and consistently demonstrates performance improvements across multiple instance benchmarks, covering virtual projection tasks. Improvements in mesh topology reconstruction, as measured by the L1 Chamfer distance (CD) metric, are consistently higher, while the system also achieves significant transmission speedups—up to 56.5×—near-instant importing—along with lowering latency in practical rendering on virtual reality (VR) devices. This result highlights that refining mesh binding improves re-creation fidelity. Our approach to scalability leads to faster user engagement and allows automated deployment without requiring human intervention during importing.
Design of a machine learning model for predicting credit risk in microfinance using environmental data Alvarez, Eladio Alfredo Soto; Lengua, Miguel Angel Cano
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9848

Abstract

Agricultural microfinance is a sector that is significantly impacted by climate-related risks, such as temperature fluctuation, soil degradation, and irregular rainfall. These environmental factors have not only impact on crop yield but also results in influencing borrowers’ ability to repay agricultural loans. Traditional credit scoring models lack in predicting due to the complex interplay between environmental and borrower-specific variables. This research study proposes a new predictive machine learning model based on XGBoost for assessing the credit risks in agricultural microfinance. This model utilizes environmental indicators, borrower characteristics, and loan attributes for computing the continuous credit risk score. The model was trained utilizing a real-world dataset of 142,017 loan applications with a 70/30 split. When compared with other traditional models, the results of the model showcases an accuracy of 99%, a recall of 84%, a precision of 89%, and an F1-score of 86%, outperforming traditional algorithms such as logistic regression and decision tree. This model has substantial implications for microfinance organizations. With this model, borrowers can evaluate risk accurately during the loan application stage by utilizing environmental data, resulting in better loan targeting, enhanced financial inclusion, and better risk mitigation for vulnerable farming communities in climate-sensitive regions.
Modeling broken rotor bar faults in induction motors: a combined SSFR and FEM approach Mabrek, Abdelhakim; Elyazid, Zaidi; Selmoune, Bachir; Hemsas, Kamel Eddine
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10455

Abstract

The fault of broken rotor bar (BRB) yields to high levels of stress in induction motor drive (IM) and being a common fault. This paper proposes a novel hybrid approach combining standstill frequency response (SSFR) testing and finite element method (FEM) modeling to improve fault diagnosis accuracy. The findings were verified experimentally using a 7.5 kW three-phase IM by SSFR approach under various failure scenarios. Reliability of SSFR method is confirmed by the use of FEM, flux 2D magnetic analysis software is employed on the same IM using in SSFR to determine the magnetic field under different fault and load conditions. The work is finished by current harmonics analyses and the outcomes of the BRB model demonstrate that the combined method enhances fault detectability, particularly for incipient and partial bar breakages, reducing false alarms compared to conventional techniques.
Optimizing tilt angle of the roof for the best performance ratio of rooftop photovoltaic Dani Ali, Nurhalim; Bahri, Syaiful; Ikhwan Siregar, Yusni; Suwondo, Suwondo; Anhar, Anhar; Lysbetti Marpaung, Noveri; Muhammad, Juandi; Hasyim Rosma, Iswadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11126

Abstract

Rooftop photovoltaic (RPV) systems are becoming increasingly popular as a source of renewable energy. One key factor that significantly affects the performance of RPV systems is the tilt angle of the solar panels. This study aims to determine the optimal tilt angle to maximize electricity production for the RPV system installed on academic building, Department of Electrical Engineering, Faculty of Engineering, University of Riau. The research method used is the PVSyst simulation. The simulation data input was used as daily weather for one year. In this study, variations in roof tilt angles of 5°, 10°, 15°, 20°, and 25° were examined. The results show that the optimal tilt angles for this location are 5° and 10°. At a 5° tilt angle, the RPV can generate 247,128 kWh per year with a performance ratio (PR) of 83%. And then at a 10° tilt angle, the system can generate 248,012 kWh per year with a PR of 82%. Based on the simulation results, other tilt angles also produced higher energy outputs but yielded lower PR values. This study provides practical recommendations for designing RPV systems in regions with similar weather conditions.

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