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 75 Documents
 1   2   3   4   5 
Search results for , issue "Vol 15, No 2: April 2026" : 75 Documents clear
Uplink power control for multi-path channel estimation in massive multiple-input multiple-output systems Amadid, Jamal; Khabba, Asma; Ouadi, Zakaria El; Sellak, Lahcen; Bousrout, Abdelmouttalib; Zeroual, Abdelouhab; Sutikno, Tole
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10707

Abstract

Multi-path communication channels provide a realistic representation of transmitter-receiver communication in practical propagation environments. This work investigates uplink (UL) multi-path channel estimation (CE) in a massive multiple-input multiple-output (M-MIMO) multi-cell multi-user system, where each user communicates with its serving base station (BS) through a multi-path channel. The network operates using time-division duplex (TDD), exploiting channel reciprocity between the uplink and downlink. The impact of multi path propagation on CE is analyzed using two approaches: the ideal minimum mean square error (MMSE) estimator and a proposed simplified estimator. The MMSE estimator assumes prior knowledge of the large-scale fading (LSF) coefficients of interfering users, which is impractical in real systems. To overcome this limitation, a simplified estimator is proposed that does not require such information while achieving asymptotic performance close to that of the MMSE estimator. Realistic propagation scenarios are also considered, where channels may include either non-line-of-sight (NLoS) components or a combination of line-of-sight (LoS) and NLoS paths depending on the user’s distance from the BS. Furthermore, a heuristic power control strategy is introduced to mitigate pilot contamination, particularly for cell-edge users, thereby reducing inter-cell interference and improving overall system performance. Analytical and simulation results validate the proposed approach.
Incubator with food object detection based automatic heating process using internet of things technology Supriyono, Heru; Supardi, Agus; Kurnia, Pramudya; Nugroho, Mohammad Dwiki Aji; Ananta, Muhammad Satria; Hidayatullah, Helmi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11297

Abstract

Conventional incubators for heating food generally can be operated manually by both its temperature level and heating duration. This condition provides risk that the food will be heated inappropriately both at its temperature level and heating duration which in turn can decrease food quality. The objective of this article is to develop an incubator for heating food which is equipped with a food object detection based automatic heating process and using internet of things (IoT) technology for distance monitoring and control, which detects the type of food and heating it automatically at both temperature level and duration. The incubator was developed using components available in the market including GY-906-DCI sensor, OV5647 camera, Raspberry Pi 4, NodeMCU microcontroller, and Telegram bot. Testing results showed that the developed incubator system was able to detect food types in real-time, as well as automatically set temperature and incubation time parameters based on it. It was able to maintain a stable incubation temperature, with an average error of 2.74% and a temperature deviation of 1.23 °C. The developed incubator potentially improves safety and user convenience where the food is heated properly both at its temperature level and heating duration.
Toward a business intelligence framework for e-government interoperability: data integration and decision support Barakat, Oumkaltoum; Elbeqqali, Omar; Chakir, Loqman
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.7918

Abstract

In e-government systems, achieving smooth data interoperability is still a major challenge, especially in judicial systems where fragmented data management affects decision-making and service quality. In order to facilitate real-time data integration and interoperability, this paper suggests a novel service-oriented business intelligence (SOBI) framework coupled with a common central data warehouse (CCDW). Our approach, which uses the Moroccan legal system as a use case, makes use of materialized views and extract, transform, and load (ETL) procedures to guarantee scalable, secure, and efficient exchange of information between disparate systems. The framework provides a scalable model for e-governance while improving decision support, transparency, and service delivery. Findings highlight enhanced system performance and data sharing, with implications for broader e-government applications. The proposed approach provides a foundation for future extensions toward internet of thing-based urban planning and real-time data integration for smart and sustainable urban ecosystems.
Gamified learning in virtual reality: a scoping review (2010-2025) Fataftah, Fuad Manna; Mohd Hashim, Siti Hazyanti
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10629

Abstract

This paper provides a scoping review of gamified learning in virtual reality (VR) from 2010-2025, which combines trends from various disciplines, game types, and learning approaches. This paper differs from previous ones, which mainly focus on bibliometric results, by critically examining 108 publications to determine the dominant types of VR games, their correlation with learning outcomes, and the impact on the learning process. This paper follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) protocol. The findings suggest that simulation, role-playing, and problem-solving VR games are the most common, particularly in healthcare and science, technology, engineering, and mathematics (STEM) fields, improving engagement, retention, and skill development. The use of VR games in other fields and the needs of neurodiverse or physically disabled learners have yet to be explored. The review discusses the use of big data and cloud computing for VR deployment and adaption, and the significance of low and no-code technologies for educators developing VR without programming knowledge. Through the synthesis of patterns, research gaps, and cross-disciplinary challenges, this study provides a roadmap for VR-based gamification research, with emphasis on inclusivity, ethical considerations, long-term learning effectiveness beyond novelty effects, and sustainable educational integration.
Attention-enhanced wasserstein GAN for agricultural market data imputation Shabrina, Ulima Inas; Sarno, Riyanarto; Anggraini, Ratih Nur Esti; Haryono, Agus Tri
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10549

Abstract

Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.
Multi objective energy aware integrated cloud scheduling with a consensus-based security Pasha, Fairoz; Natarajan, Jayapandian
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9885

Abstract

This research presents a multi-objective, energy-aware workflow scheduling framework for heterogeneous cloud–edge environments that addresses both efficiency and data integrity challenges. Conventional encryption-based security mechanisms, although effective in protecting data during task offloading, often introduce significant computational and communication overhead, leading to degraded system performance. To overcome this limitation, this work proposes the consensus security-integrity and quality-aware workflow scheduler (CSIQA-WS), which integrates energy-aware scheduling with a lightweight, consensus-driven security mechanism. The model incorporates automatic service management and an attack prevention module to detect and mitigate malicious behavior during inter-node data transmission while maintaining quality of service (QoS) constraints. A dynamic coordination between edge and cloud resources enables efficient workload distribution and robust resource utilization. Experimental evaluation using scientific workflow benchmarks demonstrates that CSIQA-WS significantly reduces processing time and energy consumption compared to existing approaches. The proposed model achieves up to 92.29% reduction in processing time and consistently improves overall QoS while preserving data integrity in dynamic execution environments. These results indicate that CSIQA-WS provides an effective and scalable solution for secure and energy-efficient workflow scheduling in modern cloud–edge systems.
Real-time sleep posture classification using wearable accelerometers and machine learning models Nguyen, Thi Thu; Quoc, Bao Bo; Prakash, Kolla Bhanu; Tran, Duc-Tan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10105

Abstract

Sleep posture plays a critical role in sleep quality and health, influencing conditions such as sleep apnea. Accurate classification of sleep postures is essential for diagnosing and treating sleep-related disorders. The sleep posture can be detected by using wearable acceleromter. This paper presents an realtime classification system for four sleep postures by integrating accelerometer data with a machine learning (ML) model. The proposed system was tested with various ML models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector classifier (SVC), and logistic regression (LR), across multiple performance metrics. The results demonstrate that the LR model, when combined with accelerometer data, significantly outperforms other methods, achieving a classification accuracy of 91%. This paper also discusses the system’s potential for real-time deployment on embedded devices, contributing to advancements in sleep posture monitoring.
A framework for multi-interval optimal power flow under solar energy penetration Maulana, Ricky; Syafii, Syafii; Aulia, Aulia
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11091

Abstract

The increasing penetration of renewable energy introduces variability and uncertainty into power system operations, thus requiring accurate forecasting methods to ensure reliable and economical scheduling. This study presents a multi-interval day-ahead optimal power flow (OPF) analysis integrated with photovoltaic (PV) generation, where hourly PV forecasts are obtained using the seasonal autoregressive integrated moving average (SARIMA) (1,0,1)(4,0,3)24 model. The forecast results achieved low error values (root mean square error (RMSE)=0.354, normalized RMSE (NRMSE)=4.192%, mean absolute error (MAE)=0.202), successfully capturing the daily PV generation pattern and providing sufficiently accurate input for the OPF simulation. The forecasted PV profiles were then integrated into a multi-interval OPF framework using the MATPOWER interior point solver (MIPS) solver. Results show that PV integration reduces system operating costs compared to cases without PV, with cost savings observed at various time intervals (e.g., reduction from $802.22/hour to $780.65/hour during PV peak hours). Compared to the conventional single-interval OPF benchmark based on Weibull distribution assumptions for PV, the proposed framework achieves lower average costs ($790.97/hour vs. $869.70/hour) while also reflecting the real variability of solar dynamics and load. Overall, the integrated forecasting-optimization framework demonstrates that SARIMA-based PV forecasting provides reliable inputs for OPF and offers a practical tool to support future system planning and operation with higher renewable energy penetration.
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning S. Kuba, Muhammad Syafaat; Faisal, Muhammad; Nurnawaty, Nurnawaty; Abdul Rahman, Titik Khawa; Syamsuri, Andi Makbul; Hayat, Muhyiddin AM; Bakti, Rizki Yusliana
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11168

Abstract

Sustainable planning of green open spaces (GOS) requires decision-making models that combine expert evaluation with public input. This study proposes a novel hybrid framework that integrates multi-criteria group decision making (MCGDM) with public sentiment analysis to support community-based and data-driven urban planning. The workflow consists of evaluating 25 community-proposed GOS locations using stepwise weight assessment ratio analysis (SWARA) for criteria weighting and MABAC-BORDA for multi-criteria ranking, resulting in 11 feasible alternatives. To incorporate community perspectives, a term frequency-inverse document frequency-support vector machine (TF-IDF–SVM) classifier was applied to 1500 public comments, where SVM achieved the highest accuracy (0.80–0.96). The integrated approach improves ranking stability, reduces decision ambiguity, and strengthens alignment between expert judgment and community sentiment. This study contributes a transparent, participatory decision-support model that unifies MCGDM and sentiment analysis to enhance the effectiveness of sustainable GOS planning.
Resource optimization of approximate convolutional neural network-based accelerator on FPGA platform Puttaswamy, Pooja Alana; Kamath, Kasaragod Poornima; Jainapur, Priyadarshini; Kumar, Karanam Sunil; Puttaswamy Gowd, Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11142

Abstract

A significant number of machine learning techniques use convolutional neural networks (CNN). The hardware acceleration is essential due to the tremendous computation demands of CNNs, as well as the need for improved energy performance and lower usage response time. This manuscript presents the resource-optimized CNN-based hardware accelerator on a field-programmable gate array (FPGA) platform. The design uses LeNet-5 architecture for handwritten digits classification using the MNIST dataset. The CNN accelerator uses three different optimization approaches in this work, including fixed-point (FP) data optimization with shortened bits, approximate multiply-accumulate (MAC) operations, and loop unrolling features with a pipelining mechanism to optimize the hardware resources. These approaches improve the latency and resources, and overall performance of the CNN architecture. The CNN-based accelerator is designed and implemented on the Xilinx Zynq platform with the high-level synthesis (HLS) tool. The proposed MAC unit obtains a latency of 3.58 ms, with a frequency of 120.69 MHz on chip. The design uses only 96 block random access memory (BRAMs), digital signal processing (DSP) units of 106 and obtains the accuracy of 99.01%. The proposed accelerator improves the performance over state-of-the-art accelerators in concerning the area, power and accuracy.

Page 1 of 8 | Total Record : 75

 1   2   3   4   5 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 15, No 2: April 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