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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 3,202 Documents
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Fixed-vision based automated quality inspection and robotic nut sorting with Dobot Magician Phisit Srinoi; Surasit Phokha; Viroch Sukontanakarn; Thewin Sakunbunyong; Wiriya Dangton
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.v15i4.11667

Abstract

This research presents a low-cost automated nut sorting system developed through laboratory testing to provide an affordable automation solution for small and medium enterprises (SMEs). The system integrates a fixed-vision camera with a Dobot Magician robotic arm, utilizing Python, OpenCV, and homography-based coordinate transformation for precise positioning. Performance was evaluated under three controlled lighting conditions with 25 samples each. Results indicate that lighting intensity significantly affects accuracy: low lighting (27.25 lux) yielded only 16% accuracy (mu=0.16, sigma approximately 0.3666), while high lighting (481.78 lux) suffered from overexposure and reflections. In contrast, optimal laboratory conditions (129.71 lux) achieved 100% classification accuracy (mu=1, sigma=0), demonstrating perfect consistency and stability. The study concludes that while the system offers a high-efficiency, budget-friendly alternative for SMEs, maintaining controlled, optimal illumination is critical for operational success. These findings provide a technical foundation for implementing cost-effective robotic sorting in real-world SME environments where high-cost sensor arrays are not feasible.
Personality prediction system using machine learning approaches: a comparative study Angad Singh; Priti Maheshwary; Nitin Kumar Mishra; Neerja Dubey
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.v15i4.11277

Abstract

Identifying personality traits from text offers valuable insights for human resource management, customer service, political campaigning, healthcare, fraud detection, and risk assessment. In psychology, personality prediction from text is an important area with the Big Five model, among the leading frameworks. Popular datasets for this task include Essays. Past works have primarily relied on conventional machine learning (ML) models using linguistic features. This paper evaluates and contrasts ten ML classifiers ‘effectiveness for personality prediction using the Essays dataset. The support vector machine (SVM) classifier yielded the best overall performance, a mean accuracy of 57.68% and means F1-score of 61.16% across all five personality traits, and outperformed logistic regression (LR). Thereby demonstrating its superior predictive capability for this task and significance as an interpretable baseline for future deep learning integration.
Scoring smarter: deep learning-based basketball scoring detection in real time Faisal Alzyoud; Monther Tarawneh; Mohammad Alkhazaleh; Mahmoud Baklizi; Nashwan Abdallah Nashwan
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.v15i4.11374

Abstract

Basketball scoring detection is challenged by scene complexity, varying camera angles, and occlusions. This paper presents a real-time basketball scoring system that combines you only look once version 8 (YOLOv8) for hoop detection, a vision transformer (ViT) for spatiotemporal motion modeling, and deep simple online and realtime tracking (DeepSORT) for object tracking, in order to overcome the accuracy loss that classical systems suffer during dynamic gameplay. The system was evaluated on a custom dataset of 4,000 annotated images and five full-length broadcast videos containing 44 verified scoring events. The proposed YOLOv8+ViT+DeepSORT model achieved 96.84% average precision at 50% intersection over union (AP50), 95.2% precision, 93.8% recall, 94.5% F1-score, and 94.55% accuracy, while sustaining 85 frames per second (FPS). Comparison against YOLOv3 with frame differencing (baseline), you only look once version 8 nano (YOLOv8n) with bidirectional feature pyramid network (BiFPN) and global attention module (GAM) attention mechanisms, and BiFPN, GAM, and SimC2f-YOLO (BGS-YOLO) confirms the best overall balance across all indicators. ViT captures the temporal dynamics of motion while DeepSORT preserves object identity across frames. The framework is therefore well suited to sports analytics, automated highlight generation, and referee assistance systems where accuracy and real-time performance are essential.
Role of decision style and knowledge in awareness influence to phishing detection in higher education Muh. Bafadhal Kurnia Alamsyah; Candiwan Candiwan
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.v15i4.10199

Abstract

Phishing attacks are a significant cybersecurity threat in higher education, where employees handle sensitive data such as student records, financial transactions, and research information. This study investigates the relationship between information security awareness (ISA), intuitive decision style (IDS), and phishing detection confidence (PDC), while assessing the moderating role of phishing email knowledge (PEK). A quantitative approach was employed, using a structured questionnaire distributed to 120 employees at XYZ University in Indonesia. Data were analyzed using moderated mediation analysis with PROCESS Macro. The findings indicate that ISA positively influences IDS, but an overreliance on intuition negatively impacts PDC. PEK significantly moderates this relationship, reducing the negative effects of intuitive decision-making. The results emphasize the importance of cybersecurity awareness and phishing-specific training programs to enhance detection confidence. Organizations should integrate security awareness programs with phishing simulations and automated detection tools to strengthen their cybersecurity defenses. This study is limited by its focus on a single organization and reliance on self-reported measures. Future research should explore additional cognitive and contextual factors influencing phishing susceptibility to develop more comprehensive security strategies.
Cross-modality brain image translation using CycleGAN for MRI to CT and CT to MRI conversion Sabura Banu Urundai Meeran; Nafeena Abdul Munaf
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.v15i4.10892

Abstract

Accurate cross-modality brain image translation between magnetic resonance imaging (MRI) and computed tomography (CT) can enhance diagnosis and treatment planning by combining complementary structural and tissue information. This study develops a cycle-consistent generative adversarial network (CycleGAN)-based framework for unpaired MRI-to-CT and CT-to-MRI conversion, eliminating the need for paired datasets. Publicly available brain imaging datasets were preprocessed with intensity normalization and resizing, and the model was trained using a U-Net generator for anatomical preservation and a PatchGAN discriminator for texture fidelity. Quantitative evaluation using structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) demonstrated that the proposed method outperforms baseline generative adversarial network (GAN) architectures, while expert radiologist reviews confirmed high structural consistency. The approach effectively maintains anatomical integrity and visual realism across modalities. This work’s novelty lies in optimizing CycleGAN architecture and hyperparameters for brain imaging translation, achieving superior performance without paired scans. The findings indicate potential for integration into clinical workflows, particularly in multimodal diagnosis and radiotherapy planning, offering a pathway to reduce scan redundancy and improve patient care. Unlike prior unpaired image-translation studies, this work introduces a U-Net–enhanced CycleGAN with optimized loss weighting and architecture tailoring for brain imaging, achieving improved structural fidelity and computational efficiency.
An energy management system enhancement for micro-grid using state-flow Abderrahmane Ouadi; Abdelkader Zitouni; Hamid Bentarzi
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.v15i4.10785

Abstract

This research focuses on the use of state-flow (SF) technique to enhance the energy management system (EMS) of a smart micro-grid that integrates multiple renewable energy sources. An EMS is responsible for efficiently balancing energy generation and electricity demand by controlling various energy sources, integrating photovoltaic (PV) system as the primary electrical source, batteries, and the utility grid or engine-generator sets as backups. A supervisory control algorithm is implemented within the EMS to optimize power flow among the micro-grid components. In this study, a control system based on the SF technique is designed, implemented, and evaluated under various operational scenarios. The proposed approach is simulated using MATLAB/Simulink and tested under different environmental and load conditions to assess its performance and validate its effectiveness. During testing, the PV system operates as the primary energy source, while the other electrical power sources function as backups. The EMS prioritizes the use of PV panels associated with the battery storage unit for satisfying the load demand. The simulation obtained results demonstrate that the proposed SF-based control approach improves the EMS performance, and hence the overall efficiency, reliability and the stability of smart micro-grid.
Utilization of augmented reality technology in overcoming educational issues in Indonesia Suharsono Bantun; Dedi Kuswandi; Saida Ulfa; Henry Praherdhiono; Jayanti Yusmah Sari; Made Duananda Kartika Degeng
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.v15i4.10156

Abstract

Education in Indonesia faces various challenges, including limited access to quality education, insufficient resources and infrastructure, a shortage of qualified teachers, and limited integration of technology in the learning process. Augmented reality (AR) offers a promising solution by combining real-world and virtual elements to create interactive and immersive learning experiences. This review identifies that AR enhances student engagement, improves understanding of complex concepts, and supports more accessible and personalized learning environments. AR also helps address infrastructure limitations through virtual simulations and mobile-based learning applications. However, challenges remain, including cost and sustainability issues, teacher training needs, infrastructure requirements, and internet connectivity. Therefore, strategic policies, collaboration among stakeholders, and the development of AR-based curriculum content are required to support sustainable implementation. Furthermore, emerging immersive technologies such as mixed reality (MR) offer additional opportunities for interactive learning; however, further empirical research is required to evaluate their effectiveness in Indonesian educational contexts.
FFR-based radio resource allocation for RIS-aided D2D communication in multicell cellular network Misfa Susanto; Soraida Sabella; Helmy Fitriawan; Ayu Purwarianti; Azrina Abd Aziz
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.v15i4.11612

Abstract

Device-to-device (D2D) communication and reconfigurable intelligent surfaces (RISs) are two technologies that are attractive components of future wireless networks. D2D allows nearby devices to communicate directly, which helps boost network efficiency by reducing the load on base stations. However, since D2D often uses the same frequency bands as regular cellular users, it can cause interferences. RIS, on the other hand, enhances signal coverage by intelligently reflecting signals toward their intended destinations, but it can also unintentionally reflect interfering signals, especially in crowded multicell networks during downlink transmission. To address these interference challenges, this paper explores the application of a fractional frequency reuse (FFR) strategy purposed for networks that integrate both D2D and RIS technologies in multicell cellular network scenarios. The simulation outcomes imply that the proposed FFR method notably expands performances i.e., raising the signal quality in term of signal-to-interference-plus-noise-ratio (SINR) from 0.47 dB to 1.93 dB, enhancing throughput from 10.8 Mbps to 13 Mbps, and improving the bit error rate (BER) to 21 errors per 100 bits, demonstrating its effectiveness in improving network reliability and efficiency.
Hybrid long range wide area network-5G-artificial intelligencearchitecture for enhanced reliable internet of things Feriel Aouissi; Farouk Boumehrez; Abdelhakim Sahour; Mohamed Lamri; Hanane Djellab; Fouzia Maamri; Abdelaali Bekhouche
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.v15i4.10758

Abstract

Internet of things (IoT) has made long range wide area network (LoRaWAN) a key component of low-power communication; nevertheless, its adoption in mission-critical domains is limited by its latency, throughput, and quality of service (QoS) restrictions. To bridge these gaps, this paper proposes a hybrid three-layer structure that combines LoRaWAN, fifth generation (5G), and artificial intelligence (AI) to achieve a dynamic trade-off between scalability, reliability, and energy efficiency. According to the findings, the proposed hybrid model significantly improves network performance. It is based on a random forest regressor that integrates LoRaWAN and 5G technologies. The model increases throughput to 40-80 Mbps while maintaining moderate energy consumption, and reduces latency from an average of over 300 ms to less than 150 ms. Additionally, the random forest algorithm improved the overall network performance stability and the packet delivery ratio to roughly 97%. This approach is more effective than earlier studies that relied on descriptive methods or a single technology. This work establishes the foundation for the applications of IoT in smart agriculture, healthcare, and fourth industrial revolution (Industry 4.0).
Comparative study of pre-trained CNN models for multiclass fault detection in solar panels Karli Eka Setiawan; Marvel Martawidjaja; Hayyun Lisdiana
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.v15i4.11191

Abstract

Solar power as renewable energy can be an alternative to fossil-based power where it is carbonless, environmentally friendly, and combats climate change. In solar panel systems, manual assessment by personnel is time-demanding and prone to human error, necessitating automated solutions. The implementation of smart systems that can automatically detect objects that hinder the solar panel from receiving solar energy can be very helpful in reducing the potential threat of decreasing performance in power generation. This study proposes a convolutional neural networks (CNN)-based image classification approach to automatically identify common solar panel conditions using visual data. The dataset used was a public dataset titled “Solar Panel Images: Clean and Faulty Images”, obtained from Kaggle, containing six classes for multiclass classification. The most effective pre-trained CNN-based deep learning model for uncovering issues in solar panels was inception-V3, achieving an overall accuracy of 91% and the highest F1-score in four categories: clean, electrical damage, physical damage, and snow coverage. These outcomes confirm the potential of implementing deep learning image classification for enhancing solar panel maintenance and monitoring systems in real-world applications.

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