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 3,202 Documents
 312   313   314   315   316 
Smart measurement revolution using long range technology and HLW8012 sensor integration Isminarti Isminarti; Nanang Roni Wibowo; Asminar Asminar; Mohamad Ilyas Abas; Muhammad Ali Chandra; Nur Azhary Iriawan Eka Putra; Widya Wisanty; Fauziah Fauziah; Deny Wiria Nugraha; Chaira Saidah Yusrie
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10823

Abstract

Long range (LoRa) has advantages compared to other communication systems such as power line control (PLC), narrowband internet of things (NB-IoT), and Sigfox. The advantage of low power consumption is the implementation and reliability of LoRa communication systems across all segments of smart grid (SG) and home automation applications. Parameters of the reliability of the communication system can also be seen from the maximum distance, signal strength, and network quality in indoor and outdoor conditions as well as different heights and levels of signal collisions when transmitted. This study aims to utilize unlicensed LoRa Shield SX1276 on the 915 MHz frequency spectrum for SG communication networks to monitor messages in the form of current, voltage, and power factor using the HLW8012 sensor. This research produces a complete LoRa communication system monitoring product according to the information needed by the user.
Decoder-only transformer with multi-scale attention for efficient printed circuit board defect inspection Chi Kien Ha; Hoanh Nguyen
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10873

Abstract

Printed circuit boards (PCBs) are critical components in modern electronics, detecting defects rapidly, and accurately is crucial in manufacturing. We propose a novel real-time PCB defect detection model based on a Transformer decoder-only architecture. The framework first extracts multi-scale features via a backbone with a feature pyramid network (FPN) and generates candidate regions using a region proposal network (RPN). These region proposals are encoded as query embeddings that drive an adaptive multi-scale deformable attention (AMDA) module in the Transformer decoder, replacing the standard encoder-decoder attention. By dynamically weighting multi-scale feature maps for each query, AMDA emphasizes the feature scale most relevant to each defect’s size and texture, and yielding enhanced discriminative representations for subtle defect detection. The decoder-only design drastically reduces computational overhead compared to full encoder-decoder Transformers, enabling real-time inference, and faster training convergence. Experiments on benchmark PCB defect datasets demonstrate that our approach outperforms state-of-the-art methods in both accuracy and speed. The proposed model’s efficiency and high precision make it well suited for deployment in fast-paced PCB manufacturing lines that demand stringent real-time performance and reliable defect detection.
Optimizing academic information systems: linking interface design with user satisfaction and loyalty Khairul Imtihan; Mardi Mardi; Amrullah Amrullah; Baiq Yulia Fitriyani; Muhamad Rodi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10160

Abstract

Academic information systems (AIS) play a crucial role in supporting academic and administrative processes in higher education institutions. Despite their widespread adoption, sustained user engagement remains a challenge, often linked to usability and system responsiveness issues. This study integrates the technology acceptance model (TAM) with experiential interface dimensions visual quality (VQ) and user interface responsiveness (UIR) to examine their influence on user satisfaction (US) and loyalty within AIS environments. Data were collected from 432 active AIS users across 12 Indonesian universities and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that perceived ease of use (PEOU) and perceived usefulness significantly enhance US, which in turn strongly predicts user loyalty (UL). System responsiveness demonstrates a substantial influence on perceived usability, while usability exerts both direct and indirect effects on long-term user commitment. Although VQ contributes positively to usability perceptions and satisfaction, its direct role in sustaining loyalty is comparatively limited. Importance–performance map analysis (IPMA) further identifies PEOU and UIR as the most strategic priorities for improving long-term engagement. By extending TAM with experiential interface factors, this study offers a more comprehensive framework for understanding AIS adoption and provides practical guidance for designing user-centered, performance-driven AIS.
Proximal policy optimization with self adaptive penalty function for vehicular resource allocation Irshad Khan; Neetha Papanna Umalakshmi; Somshekhar Durgaiah; Vijetha Acharya; Suman Joseph; Varshini Totliganahalli Rajanna
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11211

Abstract

The vehicle-to-everything (V2X) is a significant technology that improves road safety, travel experience and entertainment services. Resource allocation (RA) in vehicular networks defines the strategic distribution of communication resources like power, time slots and bandwidth among vehicles and infrastructures to provide effective data transmission. However, RA faces challenges in managing limited transmission resources because of network delays and high reception time by frequent changes in network topology and varying user demands through high mobility of vehicles. Therefore, this research proposes a proximal policy optimization with self adaptive penalty function (PPO-SAPF) based RA for vehicular communications. The PPO-SAPF optimizes the RA in dynamic vehicular networks by adjusting the coefficient matrices, which ensures better adaptability to network topology and user demands. The SAPF provides fine-tuning of policy updates, maintaining a better balance between exploration and exploitation, thereby enhancing performance under different network conditions. The PPO-SAPF achieves a less inter-packet reception time of 119 ms for 16 vehicle-to-vehicle (V2V) links in case 3 compared to context-aware RA (CARA). These results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Feature selection based SOOA-SDRGRU for DDoS attack detection and classification Teena Kodapalu Balakrishna; Swati Sharma
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11169

Abstract

Distributed denial-of-service (DDoS) attacks monitor network traffic to determine unusual patterns that represent malicious flooding. However, DDoS attacks mimic legitimate traffic which makes it difficult to accurately detect and classify between normal and attack. This research proposes Somersault Orangutan optimization algorithm for feature selection with ShakeDrop refined gated recurrent unit (SOOA-SDRGRU) to detect and classify DDoS. In OOA, somersault foraging behavior is incorporated to solve local optima issue which enhances solution diversity and rapid convergence speed toward global optima. GRU captures temporal patterns and dependencies in sequential network data, whereas ShakeDrop assist to minimize overfitting, which leads to more accurate and reliable detection of diverse attack patterns. Compared to existing methods like bacterial colony optimization (BCO) with optimized back propagation neural network (BPNN), the proposed SOOA-SDRGRU obtains high accuracy of 0.9989 and 0.9992 on CIC-IDS2017 and CIC-DDoS2019 datasets which shows robust detection method for evolving DDoS patterns.
Full bridge converter with reduced voltage stress and zerovoltage switching for LED based lighting application Sreenivasulu Meda; Ramakrishna Busharaju; Narendra Rao Rajaboyana
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11049

Abstract

Light emitting diodes (LEDs) have emerged as an energy efficient lighting source in lighting industry. However, LEDs are current controlled devices and they require efficient driver circuits to illuminate uniformly. This research work presents a full bridge-based converter to drive multiple LED lamps. In the proposed topology, two different direct current (DC) input sources are connected in anti-series across the full bridge, which significantly reduces voltage stress on the switches. Zero-voltage switching (ZVS) is achieved using a single inductor, which minimizes switching losses and improves efficiency. The converter powers three LED lamps, in which two are identical lamps. In each leg of full bridge configuration, two inductors with different current magnitudes are connected to diminish current stress of conducting device. Additional advantages include low power processing, high efficiency, pulse-width modulation (PWM) dimming, driving multiple lamps, and reduced component count. A detailed theoretical analysis of the converter’s operating modes is provided to explain its performance and behavior. The concept is validated using numerical simulations with a total power rating of 60 W to validate the steady-state operation.
Low-cost smart farming in a shared approach using internet of things and machine learning Kamlesh Ahuja; Deepika Saxena; Akash Saxena; Chandni Sikarwar; Neeraj Sharma
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11133

Abstract

In this paper, a concept of shared approach of smart farming system is discussed. The technique utilizes the ability of machine learning (ML) and internet of things (IoT) enabled sensors. Additionally, to implement this approach a layered architecture is also proposed. The layered architecture is providing the steps to achieve this model practically by using a cooperative manner. This technique is providing the smart farming services as plug and play basis. Therefore, the complexities of management and implementation is also considered at the billing and maintenance level. Additionally, a conceptual solution for the particular complexity is also suggested. The model can also motivate the new entrepreneurs to adopt the model and serve for the smart farming as service.
Driver drowsiness detection using YOLO based deep learning models Helmi Wibowo; Muh Irhas Rafiqi
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10680

Abstract

The incidence of traffic accidents in Indonesia has been escalating, predominantly attributed to human factors such as fatigue and drowsiness. This study presents the implementation of a deep learning-based drowsiness detection system utilizing the you only look once (YOLO) architecture to enhance vehicular safety. Three YOLO model variants (YOLOv5, YOLOv8, and YOLOv10) were evaluated using a dataset comprising 1,000 annotated images across four classes: alert, low vigilance, drowsy, and microsleep. A quantitative experimental methodology was employed, with performance assessed through precision, recall, accuracy, and F1-score metrics. Experimental results demonstrate that YOLOv8 (medium and small variants) achieved superior overall performance, exhibiting a balanced optimization across all evaluation metrics. YOLOv5 yielded the highest recall, suggesting its suitability for comprehensive detection tasks, whereas YOLOv10 demonstrated enhanced computational efficiency without significant performance degradation. Based on these findings, YOLOv8 is recommended as the most effective model for real-world deployment, while YOLOv5 and YOLOv10 offer viable alternatives depending on specific operational requirements. This study contributes to the advancement of early warning systems for driver drowsiness detection, with the broader aim of mitigating traffic accident risks.
Tuberculosis severity classification from exhaled breath using an electronic nose system with Inception-1D and ResNet-1D Dava Aulia; Riyanarto Sarno; Muhammad Rivai; Muhammad Amin; Alfian Nur Rosyid; Kelly Rossa Sungkono
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12212

Abstract

Exhaled breath contains volatile organic compounds (VOCs) that can be analyzed for tuberculosis (TB) detection. Electronic nose systems have demonstrated promise for this application; however, accurately distinguishing between healthy individuals and TB patients with different severity levels, namely, low and high TB, remains challenging and requires advanced deep-learning methods. Unlike previous studies that focused on binary TB detection, this study proposes an electronic nose system combined with one-dimensional deep learning models, including residual network (ResNet), visual geometry group (VGG), EfficientNet, and Inception architectures, for multiclass classification of healthy individuals and TB severity levels using exhaled-breath analysis. The results show that Inception-1D and ResNet-1D achieve the best performance for healthy and TB classification, each attaining an F1-score of 94.99%. For TB severity classification, ResNet-1D outperforms other models with an F1-score of 68.50%. Meanwhile, Inception-1D yields the highest performance on the healthy and TB severity classification dataset, with an F1-score of 74.07%. Moreover, dimensionality reduction using principal component analysis (PCA) reduces the healthy and TB dataset to ten principal components and improves the F1-score to 95.64% with Inception-1D. Overall, the proposed framework successfully captures distinctive gas sensor-response patterns associated with TB presence and severity and may support clinical decision-making in resource-limited healthcare settings.
Dimensionless description of non-isothermal fixed-bed catalytic reactors Gulzukhra Turymbetova; Zhanat Umarova; Meruyert Yurtseven; Gamidulla Tileuov; Gaziza Yelbergenova; Ainur Bekzhigitova; Gulayna Beisenova; Mohamed Othman
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12205

Abstract

This paper presents a dimensionless reaction–diffusion–energy model for a non-isothermal fixed-bed reactor. The model integrates chemical kinetics, mass transfer, heat transfer, and wall heat exchange within a unified framework. It is formulated in terms of dimensionless parameters, including the Damköhler, Peclet, Lewis, and Biot numbers, enabling the analysis of coupled transport and reaction processes within a consistent parametric space. To evaluate the stability of operating regimes, an integral energyefficiency index ne is introduced, quantifying the balance between heat generation and heat removal. Numerical results show that increasing the Damkohler number enhances conversion but also intensifies temperature gradients. Higher temperature sensitivity leads to stronger thermal feedback and the formation of pronounced temperature maxima. An optimal operating region (Da=2) is identified, where ne reaches a maximum (0.74–0.76), indicating that maximum conversion does not coincide with maximum energy efficiency. Model validation against data from an industrial phosphine oxidation reactor shows deviations within 5–7%, confirming its predictive capability. The proposed approach can be used for reactor design, optimization, and thermal regime analysis, providing a computationally efficient alternative to more complex models while preserving physical fidelity.

Page 314 of 321 | Total Record : 3202

 312   313   314   315   316 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 4: August 2026 Vol 15, No 3: June 2026 Vol 15, No 2: April 2026 Vol 15, No 1: February 2026 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