cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 270   271   272   273   274 
Evaluating digital competency among statistical educators: a comparative analysis of input-oriented DEA models Faezah Mohamad Razi, Nor; A. Wahab, Jufiza; Zafirah Azmi, Anis; Baharun, Norhayati; Masrom, Suraya; Aslily Sarkam, Nor
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.v14i5.8106

Abstract

As the educational landscape shifts towards online learning, assessing educators' digital competencies has become crucial. This study aims to evaluate the digital competencies of university educators using data envelopment analysis (DEA), specifically comparing the banker, charnes, and cooper (BCC) input-oriented models (super efficiency and Bi-O multi-criteria data envelopment analysis (MCDEA) super efficiency BCC models). The research was conducted in three phases. Initially, the BCC model assessed educators' digital competencies. Subsequently, the Bi-O MCDEA model evaluated these competencies within an online learning context. Finally, the effectiveness of the two models was compared. Data was collected through a survey administered to 30 educators from Universiti Teknologi MARA, with a response rate of 75%. Results showed that while the BCC model identified 23 out of 30 educators as efficient, the Bi-O MCDEA model recognized only two as efficient. This discrepancy highlights the different stringencies of the models and their impact on assessing digital competencies. The super efficiency (SE) model was then used to rank the efficient educators to determine the most proficient. The study underscores the need for precise assessment tools in online education to enhance digital competencies effectively. It suggests that integrating advanced DEA models can significantly improve the identification and training of educators, thereby enriching the educational outcomes in digital environments.
Smart virtual rotor for frequency stability enhancement considering inverter-based renewable energy sources Setiadi, Herlambang; Nuris Syifa, Baity; Abdillah, Muhammad; Afif, Yusrizal
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.v14i5.9648

Abstract

This paper proposes a novel smart virtual rotor controller (VRC) that combines the Bat Algorithm (BA) with extreme learning machine (ELM) to enhance frequency stability in power systems. To reflect the impact of renewable integration, inverter-based power plants are incorporated to simulate high levels of penetration from power-electronics-based generation. The proposed method first tunes the virtual rotor parameters (virtual inertia and damping control) using BA under varying operating conditions. These parameters are then trained with ELM to enable adaptive control across different scenarios. Time-domain simulations demonstrate that the proposed approach outperforms existing methods in terms of frequency nadir and settling time, while also achieving a significant reduction in execution time, requiring only 0.0033 seconds.
An internet of things-enabled wearable device for stress monitoring and control Tyulepberdinova, Gulnur; Abduvalova, Ainur; Kunelbayev, Murat; Amirkhanova, Gulshat; Adilzhanova, Saltanat
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.v14i5.9599

Abstract

The development of a wearable sensor device integrated into the internet of things (IoT) infrastructure is presented, with functionality aimed at continuous measurement of the user's physiological parameters and their intelligent processing for real-time stress level assessment. The system enables continuous monitoring of physiological parameters, allowing early detection of stress signals and supporting adaptive behavioral responses. The hardware platform is designed to consolidate various biomedical sensors, enabling continuous acquisition and intelligent processing of physiological data in real time. During testing, heart rate (HR) ranged from 68 to 89 beats per minute (bpm), respiratory rate varied from 11 to 15 breaths per minute, and skin conductivity ranged from 63 to 77 µS. Acquired physiological data were uploaded to a cloud-based infrastructure to enable advanced processing and analysis. The system achieved an overall stress detection accuracy of 87%, and signal stability remained high even under changing conditions. The proposed wearable solution demonstrates strong potential for use in healthcare, education, and occupational environments. It also offers scalability through the integration of intelligent algorithms and additional sensor modules.
Mobile application to optimize appointment management in a specialized dental center Urbina-Novoa, Joel; Cabanillas-Carbonell, Michael
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.v14i5.9175

Abstract

The objective of the research was to implement a mobile application for the management of appointments in a specialized dental center, to improve patient care, allowing them to make their reservation from the place and at the time they want. The research has a quantitative approach, of experimental type with a pre-experimental design. The population consisted of 70 patients, with a total sample of 60. The SPSS statistical software was used for the elaboration of the results, obtaining positive results. With all the above mentioned in this research work, it is concluded that the implementation of the mobile application for appointment management for the dental center will facilitate patients to have better attention, which allows a reduction of time and satisfaction with the service. In indicator 1, referring to appointment registration time, a reduction of 44.13% was obtained. In indicator 2 on the number of patients presenting for appointments, an increase of 17.58% was obtained, and finally, in indicator 3 on the level of satisfaction, an increase of 65% was obtained.
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.

Page 272 of 291 | Total Record : 2901

 270   271   272   273   274 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 6: December 2024 Vol 13, No 5: October 2024 Vol 13, No 4: August 2024 Vol 13, No 3: June 2024 Vol 13, No 2: April 2024 Vol 13, No 1: February 2024 Vol 12, No 6: December 2023 Vol 12, No 5: October 2023 Vol 12, No 4: August 2023 Vol 12, No 3: June 2023 Vol 12, No 2: April 2023 Vol 12, No 1: February 2023 Vol 11, No 6: December 2022 Vol 11, No 5: October 2022 Vol 11, No 4: August 2022 Vol 11, No 3: June 2022 Vol 11, No 2: April 2022 Vol 11, No 1: February 2022 Vol 10, No 6: December 2021 Vol 10, No 5: October 2021 Vol 10, No 4: August 2021 Vol 10, No 3: June 2021 Vol 10, No 2: April 2021 Vol 10, No 1: February 2021 Vol 9, No 6: December 2020 Vol 9, No 5: October 2020 Vol 9, No 4: August 2020 Vol 9, No 3: June 2020 Vol 9, No 2: April 2020 Vol 9, No 1: February 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 List of Accepted Papers (with minor revisions) More Issue