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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 3,126 Documents
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Accurate hybrid prediction model for poverty line, number, and percentage of impoverished individuals Toni Wijanarko Adi Putra; Yohanes Suhari; Achmad Solechan; Solikhin Solikhin; M. Zakki Abdillah
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10533

Abstract

Poverty remains a major social issue in many developing countries, including Indonesia, as seen in the Central Java region. Over the last five years, the number of impoverished people in Central Java has shown fluctuations, with data from the Central Statistics Agency indicating figures of 3,897.20 thousand (2018), 3,743.23 thousand (2019), 3,980.90 thousand (2020), 4,109.7 thousand (2021), and 3,831.44 thousand (2022). Analyzing these trends is crucial for future poverty reduction efforts. This study aims to develop a web-based predictive system capable of forecasting the poverty line, as well as the number and percentage of poor residents in Central Java. The research utilizes a hybrid forecasting model that integrates the Holt-Winters triple exponential smoothing (HWTES) method with fuzzy time series (FTS), alongside algorithmic approaches such as rate of change (RoC) and frequency-based segmentation. The model's accuracy, evaluated using the average absolute percentage error (MAPE), shows low error rates: 0.9% for the number of impoverished people, 1.6% for the percentage, and 0.7% for the poverty threshold. Compared to the standard HWTES model, this hybrid model demonstrates greater precision. As a result, it can serve as an effective tool to support strategic planning and enhance poverty alleviation programs in Central Java.
Hybrid AI-driven anomaly detection and sequential attack classification for securing IoT networks Gauri Sameer Rapate; Ambuja Krishnappa; Sarala Duggonahalli Veeresh; Karanam Sunil Kumar; Bellary Kursheed
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11048

Abstract

Internet of things (IoT) systems are often inherently heterogeneous and the constantly evolving cyber threat presents a variety of attack vectors that can expose sensitive data across multiple mission-critical applications. The existing intrusion detection methods are often prone to zero-day attacks and specific to limited known intrusions. This paper designs a hybrid and multi level cyber-threat detection framework based on the robust data preprocessing scheme, correlation-based optimal feature selection and integrated anomaly and intrusion detection using a supervised learning approach. In the first stage, a random forest (RF)-based binary anomaly detector is designed as a fast primary threat filter against zero-day threats by detecting traffic anomalies without any prior attack signal. In the second stage, an adaptive, time-aware long short-term memory (LSTM) model performs multi-class intrusion classification using time-lag analysis in traffic flows to accurately identify and classify known attack types with high precision. The proposed framework is evaluated on the network flow telemetry of network–internet of things–version 2 (NF-ToN-IoT-V2) dataset and achieved 99% accuracy in both binary and multiclass settings, with a lower response time of 7.8 ms.
A smart parcel locker system with parcel status and image notifications via LINE application Chaiyong Soemphol; Chonlatee Photong; Piyapat Panmuang
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10683

Abstract

During the COVID-19 pandemic, online shopping grew rapidly, leading to more parcel deliveries. However, recipients are not always home to receive them. Some existing systems send alerts via SMS or email, but these methods often incur additional costs. To address this issue, we developed a smart parcel box. It uses IoT and solar power to work efficiently and sustainably, and it sends free delivery alerts through the LINE notify app, which is popular in Thailand. The prototype was tested using four parcel sizes: CD-sized (15×15×15 cm), 0+4 (17×11×10 cm), 2A (14×20×12 cm), and M-sized (27×43×20 cm). Results showed that the 0+4 parcel size had the fastest average detection and notification time of 8.42 s, followed by CD sized (9.81 s), 2A (9.9 s), and M-sized parcels (10.1 s). The M-sized parcel had the lowest standard deviation (SD) (0.42 s), while 0+4 was the least consistent (1.718 s). Differences in detection times were due to parcel size and placement but did not affect notifications. The system achieved 100% detection across all sizes, and the images allowed users to verify parcel details and sender information. These results demonstrate that the prototype is reliable, energy-efficient, and suitable for further development and adaptation in smart delivery systems.
Entropy augmented energy detection for cognitive radio: robust spectrum sensing under fading channels Lingeswari Ponnusamy; Marichamy Perumalsamy
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10059

Abstract

Cognitive radio systems provide an intelligent solution to spectrum scarcity by dynamically accessing underutilized frequency bands licensed to primary users. Among various sensing techniques, energy detection (ED) is extensively adopted due to its inherent simplicity and minimal computational requirements, but suffers from poor performance at low signal-to-noise ratio (SNR) environments under channel impairments such as additive white gaussian noise (AWGN) and multipath fading. This paper proposes an enhanced spectrum sensing approach by integrating ED with entropy-based techniques, specifically Kapur and Renyi entropy measures. The proposed methods are evaluated under AWGN and various fading environments with binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations. Simulation results demonstrate substantial improvements in detection performance. The results show that entropy-based enhancements significantly improve the reliability of spectrum sensing in cognitive radio (CR) systems operating under challenging channel conditions. Among the fading models, the Nakagami channel causes the greatest degradation in detection probability, followed by the Rayleigh fading channel. ED with Renyi entropy improves Pd by 15-fold and 8-fold, compared to ED under Nakagami and Rayleigh channels respectively.
Advanced artificial intelligence-based multi-sensor fusion for environmental perception in autonomous electric vehicles Billu Naveen; Malligunta Kiran Kumar; Thalanki Venkata Sai Kalyani; Thulasi Bikku; Kambhampati Venkata Govardhan Rao
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10739

Abstract

As autonomous electric vehicles (AEVs) continue to evolve, the demand for robust obstacle detection systems becomes increasingly critical to ensure safety, efficiency, and adaptability in real-world environments. This review presents a comprehensive synthesis of recent advancements in sensor fusion technologies, emphasizing the integration of light detection and ranging(LiDAR), radar, and camera-based vision systems. It highlights the role of deep learning architectures—such as you only look once (YOLO), convolutional neural networks (CNNs), and related neural models—in enhancing object detection, classification, and segmentation. The review categorizes key research themes, including fusion methodologies, real-time processing, edge computing, performance in adverse weather conditions, pedestrian detection, and sensor calibration. Special attention is paid to techniques that merge spatial, velocity, and semantic data to mitigate individual sensor limitations. The paper also discusses hardware-accelerated solutions for low-latency inference and the use of lightweight models for deployment on edge devices. Benchmark datasets, of vehicle-to-everything (V2X) and internet of thing (IoT)-based infrastructure, and calibration challenges are examined for their roles in ensuring accuracy and reliability. Drawing from over 100 referenced studies, this work serves as a foundational resource for researchers and developers aiming to advance artificial intelligence (AI)-based sensor fusion systems in next-generation AEVs.
Analysis of machine learning methods for detection of cataracts Anastassiya Tyunina; Sabina Rakhmetulayeva; Eduard Schiller
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10444

Abstract

Cataracts remain the leading cause of visual impairment worldwide. We focus on improving the you only look once (YOLO) architecture through targeted optimization to enhance feature extraction. We trained the optimized YOLOv8 detector using 11,274 annotated fundus and anterior segment images. During training, five-fold cross-validation, color magnification, and stochastic weight averaging (SWA) were applied to ensure convergence. In the external test set, the model achieved an F1-score of 98.9% and an mAP50 of 0.995. On an NVIDIA RTX A2000 GPU, the inference speed reached 520 frames per second. Our network enables real-time cataract diagnosis on low-cost GPUs, surpassing previous ResNet- and MobileNet-based benchmarks by ?4% in F1-score and reducing output latency by 68%.
Spatio-temporal pedestrian detection in video: comparative evaluation of VGG16 with recurrent neural networks Tanya Gupta; Neera Batra
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10406

Abstract

Pedestrian detection is a crucial application in video surveillance, autonomous driving, and traffic monitoring. Thus, reliable surveillance is required for individual decision making and safety. The study aims to compare two models, one based on VGG16 for feature extraction, coupled with a long short-term memory (LSTM), and the other simply a dense model, for pedestrian detection in video. The integration of an attention mechanism to improve feature discrimination across frames along with a lightweight structure for real-time processing that enables cross-domain generalization to diverse datasets is novelty of this work. We exploit the pre-trained VGG16 model on ImageNet, extracting spatial features from all the frames of the videos. We then feed these spatial features through an LSTM to capture temporal dependencies. The dense model uses just the spatial features and throws into the bin of information the time holds for them. We apply accuracy, precision, recall, and specificity as metrics in evaluation models on a labeled dataset of pedestrian video clips. Experimental results show that the VGG+LSTM model performs better than the dense model by giving a higher accuracy and performing better on temporal variations of frames. The LSTM-based approach achieves 0.96 accuracy over multivariate datasets.
A proactive approach to software security using DCodeBERT for vulnerability management Indurthi Ravindra Indurthi; Shaik Abdul Hameed; Polasi Sushma; Jose Pitchaiya; Veeramreddy Surya Narayana Reddy; Maganti Syamala
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11100

Abstract

The complexity of modern software has increased security risks, emphasizing the need for automated detection and correction. DCodeBERT, a CodeBERT-based vulnerability detection and remediation framework, is introduced in this study. DCodeBERT uses a multi-task learning framework with shared-private layers, gradient normalization, and uncertainty weighting to stand out. This architecture lets the model capture general representations while preserving task-specific details. From open-source repositories and vetted vulnerability databases, 85,000 code snippets—vulnerable, clean, and repaired—were collected. C, C++, Java, and Python programming languages (PLs) make this dataset highly usable. DCodeBERT surpasses CodeGPT, VulDeePecker, CodeT5 Small, GraphCodeBERT, and Devign in accuracy, precision, recall, and F1-score. Statistics show that the improvements are significant, and qualitative inspection shows that the resulting patches fix buffer overflows and injection problems within semantic validity. This novel approach combines multi-task optimization with natural and PL semantic integration for high cross-language performance. The findings show that DCodeBERT improves vulnerability management in software development settings.
Wind energy and energy potential assessment on Ambon Island Lory Marcus Parera; Abhi Rizky Heluth; Mey Chintya Yesaya; Johanis Leuwol
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10907

Abstract

Ambon Island has a high dependence on fossil fuels, which causes environmental and energy security problems, even though the potential for renewable energy, especially wind, is enormous according to national energy plan data. This study aims to assess the technical and economic feasibility of wind energy development in Latuhalat Village as a case study. Wind speed and direction data for the period January-December 2024 from the Ambon Maritime Meteorological Station were analyzed, showing that the prevailing winds are from the east and southeast. Assuming an air density of 1.225 kg/m³, a turbine efficiency of 35%, and a rotor sweep area of 78.54 m², the estimated annual energy reaches 24,528 kWh. A capacity factor of 30% results in a realistic output of 7,358 kWh/year. A horizontal-axis wind turbine with inverse taper blades is recommended to suit local wind characteristics, producing 0.7-46.8 kW of power with a system efficiency of 67%. This study concludes that Latuhalat Village has viable wind energy potential for further development. Its implementation requires a holistic approach encompassing technical, economic, social, and policy aspects to support sustainable energy transition on Ambon Island.
Comparative analysis of modular outer rotor and modular inner rotor permanent magnet flux switching machine Roshada Ismail; Erwan Sulaiman; Syed Muhammad Naufal Syed Othman; Nur Afiqah Mostaman; Irfan Ali Soomro
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8456

Abstract

A modular rotor is an advanced design in electric motor (EM) technology, offering enhanced performance and efficiency for various applications. Its segmented structure allows for optimized magnetic flux paths, reducing iron losses and improving energy efficiency. The modular rotors can be classified into modular inner rotors and modular outer rotors (ORs). While both have their applications, modular inner rotor permanent magnet flux switching machines (PMFSMs) face limitations, including lower torque density, inefficient magnetic flux operation, and reduced cooling capability, making them less suitable for high-torque applications. This paper presents an analysis of the comparison between a new modular OR and a modular inner rotor PMFSM. The outer OR-PMFSM offers higher torque density due to more efficient magnetic flux positioning, resulting in a better torque-to weight ratio. The design and analysis are conducted using JMAG designer version 18 under no-load and load conditions to test the proposed design's effectiveness. Hence, the suggested design obtained an increment of torque and power of 6.06% and 5.4%, respectively. In conclusion, the modular OR produces higher torque and power than the modular OR. The proposed motor can be very useful in electric bike applications for practical high performance and low cost.

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