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
 3   4   5   6   7 
Search results for , issue "Vol 14, No 2: April 2025" : 75 Documents clear
Internet of things-drone trajectory planning model with edge computing based on long range payload in rural areas Prasetyo Nugroho, Eddy; Djatna, Taufik; Sukaesih Sitanggang, Imas; Hermadi, Irman
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8776

Abstract

The integration of internet of things (IoT) with unmanned aerial vehicle (UAV) or drone, for precision agriculture (PA) in rural tea plantations is required to ensure optimal outcomes. However, rural settings presents exceptional challenges for data transmission, particularly in maintaining effective communication between drone and ground control stations (GCS). Therefore, this research aimed to develop a payload metadata identification model using long range (LoRa) technology, known for robust IoT capabilities of the model. LoRa was used to transmit drone data packets to GCS, including image data computations and onboard sensor information. Additionally, the research proposed IoT-drone trajectory planning model, specifically designed for PA in rural tea plantations. This model incorporated LoRa technology for data transmission, leveraging the effectiveness of the model in remote areas. Edge computing was also integrated into model to classify the suitability of tea plantation picking areas based on image captured with drone. An important component of the research was trajectory planning system, which optimized drone flight paths by considering location data, throughput data, battery energy consumption, and the computation of suitable picking locations. Finally, experimental results showed the effectiveness of the proposed model in identifying payload metadata, monitoring drone trajectory, and optimizing picking location paths in rural tea plantations.
Optimizing turbine location in upgraded wind farm using grasshopper optimization algorithm Nguyen, Khoa Dang; Trung Tran, Tinh; Vo, Dieu Ngoc
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8869

Abstract

This research explores the use of the grasshopper optimization algorithm (GOA) for optimizing the placement of additional turbines in an established wind farm. The primary objective is to increase the annual energy production (AEP) of the wind farm while minimizing the wake effects caused by both existing and new turbines. The research evaluates three different turbine types (1.5 MW, 2.0 MW, and 2.5 MW) to identify the most appropriate choice for increasing the wind farm's capacity. The GOA’s performance is compared with the commercial software windPRO and validated using WAsP software for energy calculations. Numerical results indicate that the GOA effectively improves wind farm layout, with the 1.5 MW turbines identified as the optimal choice for maximizing AEP and minimizing wake interactions. This study provides practical insights for wind farm operators and contributes to the development of advanced optimization techniques in wind energy.
An efficient clustering approach in electrical energy consumption patterns Tiara Kusuma, Dine; Ahmad, Norashikin; Sakinah Syed Ahmad, Sharifah; BM Sangadji, Iriansyah; Arvio, Yozika
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8666

Abstract

A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF). The objective of this research is to determine the efficacy and benefits of each clustering technique employed in load profile analysis. The clustering algorithm approach used in this research is k-means and fuzzy subtractive clustering (FSC). The trials carried out on these two approaches provide valuable knowledge regarding the effectiveness and superiority of each algorithm in producing significant clusters from the data used in this research. The evaluation conducted using the Davies-Bouldin index (DBI) indicates that the quality value for FSC is 0.25 for business customers and 0.31 for industrial customers. On the other hand, the quality value for k-means is 0.55 for business customers and 0.56 for industrial customers.
Evaluate of vest massage therapy with rotating pressure based on pre-experimental methods Loniza, Erika; Aqmariah Mohd Kanafiah, Siti Nurul; Jusman, Yessi; Zakaria, Zulkarnay
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8622

Abstract

Many postpartum mothers complain that their milk production is too low to supply the baby’s needs. There are two essential substances in the milk: the prolactin hormone and the oxytocin hormone. Consequently, there are two ways to stimulate these hormones: massage techniques such as breast care and oxytocin massage. This study aims to design vest therapy devices to expedite breast milk production. With the use of vest therapeutic devices, it can be observed that the amount of breast milk production increases. This research uses a pre-experimental method in postpartum mothers, which uses the vest massage therapy and does not use the vest massage therapy. Accidental sampling was used as the sampling method for this study, and the data were analyzed using the independent t-test. It is hoped that making Vest therapy devices can facilitate breastfeeding for postpartum mothers with the aim that they can increase the amount of breast milk and supply the milk for the babies in the early stage of their life. The test result discovered an increase in breast milk volume in breastfeeding mothers by an average of 7.3 ml in postpartum mothers who used vest therapy equipment compared to the previous amount of milk produced.
Enhancing recommendation diversity in e-commerce using siamese network and cluster-based technique Bahi, Abderaouf; Gasmi, Ibtissem; Bentrad, Sassi; Khantouchi, Ramzi
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8572

Abstract

This study investigates the difficulty of improving product recommendations in e-commerce systems by tackling the common problem of poor diversity in suggestions. We present a novel approach that uses a siamese network architecture and ResNet for feature extraction to recommend visually similar elements while incorporating diversity through a cluster-based mechanism. The Siamese network is used to compare product pairs, allowing it to recommend both comparable and dissimilar items from distinct clusters. The model was evaluated using a variety of evaluation metrics, resulting in an accuracy of 88.5%, a precision of 90.2%, a recall of 87.1%, and an F1 score of 88.6%. Our results demonstrate that our strategy maintains a high level of relevance in suggestions while efficiently incorporating variety, hence improving the overall user experience in e-commerce applications.
Data-driven clustering of smart farming to optimize agricultural practices through machine learning Thongnim, Pattharaporn; Srinil, Phaitoon; Pukseng, Thanaphon
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9343

Abstract

This study investigates the optimization of durian farming practices in Eastern Thailand using data-driven clustering techniques. The research aims to identify distinct agricultural patterns and improve resource allocation in durian production. K-means clustering is applied to durian production area and yield data from 2012 to 2023. Cluster quality is assessed using the Davies-Bouldin index (DBI), Dunn index, and Silhouette score. The methodology included comparing clustering results before and after log transformation of the data. Three main clusters are identified which are large-scale high-yield producers, small-scale lower-yield areas, and medium-scale producers with moderate yields. Notably, log transformation did not consistently improve clustering performance with original data often producing better-defined clusters. This finding highlights the importance of carefully considering data pre processing methods. Furthermore, the data-driven clustering offers valuable insights for precision agriculture by identifying regions with higher productivity allowing for targeted interventions and better resource allocation. The results can guide farmers in optimizing durian cultivation strategies, potentially leading to increased yields and more sustainable farming practices in Eastern Thailand's durian industry.
Conceptualizing the ‘All You Can Eat’ game to promote healthy eating habits among young children Yi-Chin Liu, Noreena; Mohd Tuah, Nooralisa; Ying Chen Yi, Darren
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8711

Abstract

Childhood obesity is a growing concern globally, with unhealthy eating habits being one of the leading causes. In response, researchers and game designers have investigated the use of serious games to encourage healthy eating among young children. Creating successful serious games to encourage children's good eating habits involves thoughtful consideration of elements such as age-appropriate content, game mechanics, and motivator strategies. The aim of this project is to create a serious game design that promotes and supports healthy eating habits in youngsters. This study evaluates children's existing understanding of nutrition by gathering their comments using a serious game as an example. Various gaming elements are recognized, leading to the creation of a board game named "All You Can Eat" (AYCE). The design evaluation process involves conducting questionnaire surveys and gathering feedback from both parents and children. The results will assist future research in creating and bringing to realisation the AYCE game. This research can be extended to a range of health topics beyond healthy eating habits, such as serious games for learning about cultures and ethics. Researchers, educators, and game designers collaborate to produce unique and interesting games aimed at promoting good eating habits and preventing youngsters’ obesity.
Enhancing solar panel efficiency through dual-axis tracking and fresnel lens concentration: an image processing approach Ghozi Witsqa Ramadhan, Muhammad; Halim, Levin; Wahab, Faisal
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8465

Abstract

Solar energy is currently utilized as an inexhaustible renewable energy source. Solar panels can convert solar energy into electrical energy that humans can use. The drawback of solar panels is that they cannot always be perpendicular to the sun, causing a decrease in the intensity of incoming light. Therefore, in this research, a solar tracking system with a fresnel lens was designed using image processing to increase the output of solar panels. In this research, programming was done using Python software for image processing using the hue, saturation, value (HSV) color, and space model, which was then connected with Arduino using the PyFirmata library to move the motor. In this research, solar panels with a fresnel lens and solar tracking were implemented. Data collection was performed on the output voltage of the solar panel. The research concludes that solar panels with solar tracker and fresnel lens have a higher average output voltage of 7.53 V than passive solar panels with an average output voltage of 6.38 V. Also, the average output voltage increased by 18.02% after implementing the solar tracking system and adding the fresnel lens.
Position control to expand the headlights’ angle of a car by DC motor drive system using PSO algorithm for speed loop Thi Hoai Thu Anh, An; Cong Duc, Pham; Dinh Huan, Le
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8612

Abstract

Today, along with the robust development of society, cars are almost considered a primary means of transportation. This article focuses on designing headlight controls for older car models that are not equipped with adaptive headlight systems (AHS), which are different from modern cars such as Porsche, BMW, Audi, and Mercedes-Benz vehicles. The design is for a lighting system that operates during nighttime to improve illumination and enhance visibility in curves, with cost-effective and suitable solutions for average vehicles to ensure safety. This system uses a DC motor to control the headlight angle based on the steering wheel rotation. It is combined with the particle swarm optimization (PSO) algorithm to find the best response parameters for the proportional-integral-derivative (PID) controller. Research results on the MATLAB/Simulink and the experimental model show that the model established by this method has good accuracy, the controllers can significantly reduce the excessive deviation of the headlights’ operational precision, and traffic accidents can be minimized, increasing safety for users.
Lung diseases identification using hybrid transfer learning and bidirectional long short-term memory Eka Cahyani, Denis; Tri Oktoviana, Lucky; Yasin, Mohamad; Wahyuningsih, Sapti; Dionixius, Dionixius; Maulidaningsih, Ranti; Setumin, Samsul
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9114

Abstract

Lung diseases rank as the third most prevalent cause of mortality globally. Accurate identification of lung disease is essential to provide appropriate medical intervention for patients. This research devised a categorization system for lung diseases using chest X-Rays (CXR). The system can identify bacterial pneumonia, viral pneumonia, COVID-19, tuberculosis, and normal CXR. The approach for detecting lung diseases utilize a combination of hybrid transfer learning and bidirectional long short-term memory. The research included convolutional neural network (CNN) models including Resnet50-BiLSTM, VGG19-BiLSTM, InceptionV3-BiLSTM, Resnet50, VGG19, and InceptionV3. The Resnet50-BiLSTM model outperforms other models in terms of accuracy and overall performance. The Resnet50-BiLSTM model achieved an accuracy of 99.87%. The models that achieve the second greatest accuracy are Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, and InceptionV3. The research utilizes precision, recall, and F1-Measure to demonstrate that Resnet50-BiLSTM outperforms other methods by achieving the greatest value. This research improves the performance outcomes when compared to earlier studies.

Page 5 of 8 | Total Record : 75

 3   4   5   6   7 

Filter by Year

2025 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