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 3,049 Documents
 299   300   301   302   303 
Hybrid XAI and deep learning architecture for trustworthy dental diagnostics Fadhillah, Yusra; Hasan Siregar, Muhammad Noor; Abdul Kodir, Ade Ismail; Rizki, Khairur
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11193

Abstract

Dental periodontal disease is a persistent an inflammatory disorder affecting tooth supporting tissues and stays a main motive of tooth loss. Although dental radiographs are essential for early diagnosis, their interpretation is often subjective and inconsistent due to reliance on clinician expertise. This study proposes an automated and interpretable diagnostic framework using a convolutional neural network (CNN) integrated with gradient-weighted class activation mapping (Grad-CAM). The CNN performs binary classification of periapical radiographs into periodontal and normal categories, while Grad-CAM provides visual explanations of the model’s decision-making process. Experimental results show that the proposed model achieves a classification accuracy of 94.17%, indicating reliable diagnostic performance. The generated heatmaps consistently highlight clinically relevant regions, particularly alveolar bone loss in periodontal cases, whereas normal images exhibit no pathological activation. These findings demonstrate that the proposed CNN–Grad-CAM framework enhances both diagnostic accuracy and interpretability. The study contributes a transparent and trustworthy artificial intelligence solution to support objective periodontal disease diagnosis in dental radiology.
Hybrid metaheuristic algorithms for feature selection in classification: a systematic literature review Manal Othman; Ku Ruhana Ku-Mahamud
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10991

Abstract

Feature selection (FS) is a popular technique for improving machine learning (ML) model's effectiveness by eliminating irrelevant and redundant features. It is challenging because of the intricate relationship between features and large search space. Recent studies have focused on using hybrid metaheuristics to solve FS problem. This systematic literature review (SLR) is performed on three significant databases that explores recent studies from 2019 to 2024 that used hybrid metaheuristics for FS in classification. This paper aims to understand the existing hybrid algorithms, hybridization goal, hybridization type, and application domains. Moreover, crucial parameters, fitness and transfer functions, initial population method, traditional FS approach, classification algorithm, evaluation criteria, and statistical test are investigated in this paper. The qualitative findings derived from the systematic review encompassed 646 publications, systematically categorized based on predefined inclusion and exclusion criteria. Consequently, 35 papers were analyzed to develop new insights in the domain of FS in classification, focusing on single-objective metaheuristics. Hybrid metaheuristics surpass the efficacy of their individual components in enhancing algorithmic performance to attain optimal or near-optimal solutions. The limitations of hybrid metaheuristics and research gaps are identified for scholars interested in developing metaheuristic algorithms for FS.
Comparing the novel Dhouib-Matrix-SPP to genetic algorithm for autonomous mobile robot path planning problem Souhail Dhouib; Danijela Pezer; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11712

Abstract

Autonomous mobile robots (AMRs) are becoming integral to applications ranging from industrial automation to urban mobility. A core challenge in deploying AMRs effectively is the path planning problem determining an optimal and collision-free path from a start to a goal location within a given environment. This paper proposes a novel method, Dhouib-Matrix-SPP (DM-SPP), that enhances path planning efficiency and adaptability for AMRs operating in different statistical environment. Basically, DM-SPP is developed to unravel the shortest path in a graph and based on columns-rows structure with polynomial computational time. Here, the DM-SPP method is adapted to plan the shortest feasible path between two positions while avoiding obstacles. In order to prove the validity of the proposed DM-SPP method, it is applied to different environments and compared to different case studies taken from the literature. The simulation results show that the DM-SPP method was able to find, with a significantly lower number of iterations, the optimal solutions in comparison with other results obtained by the genetic algorithm (GA) method. DM-SPP presents an overall average improvement in computation time of (37882.55%) compared to the GA, which can reduce search and execution time.
Kernel PCA-enhanced BoVW representation for SIFT-based face recognition using SM Chen-Han Chong; Siew-Chin Chong; Lee-Ying Chong
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10946

Abstract

This study proposes a face recognition pipeline that integrates scale-invariant feature transform (SIFT) descriptors, the bag of visual words (BoVW) model, Kernel principal component analysis (KPCA), and support vector machine (SVM) classification. It starts by extracting local keypoint descriptors from preprocessed face images using SIFT. These descriptors are subsequently vector-quantized into a visual vocabulary through MiniBatch K-Means clustering, yielding fixed-length BoVW histograms for each image. Nonlinear dimensionality reduction is achieved by applying KPCA with a radial basis function, addressing the complexity of the feature space. The resulting compact feature representations are subsequently classified using a linear SVM. The proposed method is evaluated on labelled faces in the wild (LFW) dataset with filtered 100 classes, demonstrating notable classification accuracy and reliable generalization across training, validation, and testing splits. Our experimental evaluation confirms that integrating local invariant features, nonlinear feature reduction, and discriminative classification allows the proposed method to exceed state-of-the-art face recognition performance. In addition, this proposed method is particularly suitable for scenarios with limited training data and computational resources, providing a lightweight but robust alternative to deep learning-based models.
Detecting anomalies in MQTT/MQTT-SN traffic using intelligent learning models Nabeel Mustafa Alassaf; Selvakumar Manickam; Ammar Odeh; Mohammed Anbar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11889

Abstract

The widespread adoption of the internet of things (IoT) has heightened demand for secure, efficient communication across constrained devices. Lightweight protocols such as message queuing telemetry transport (MQTT) and its variant MQTT-sensor networks (SN) are widely used for IoT messaging but lack intrinsic security mechanisms, leaving them vulnerable to denial-of-service, spoofing, and injection attacks. This study presents a machine learning (ML)-based anomaly detection framework designed to enhance the security of MQTT and MQTT-SN traffic. We emulate realistic IoT environments to generate both benign and malicious traffic, extracting protocol-specific features such as packet length, topic length, quality of service (QoS) levels, and publish frequency. Three supervised models—random forest (RF), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were trained and evaluated using cross-validation and statistical performance metrics. Experimental findings demonstrate that XGBoost achieved the best overall results, with 97.4% accuracy, 95.9% F1-score, and low false-positive and false-negative rates. Furthermore, the framework was successfully deployed on edge devices such as Raspberry Pi Zero W and ESP32, confirming its real-time feasibility and efficiency. The proposed approach highlights the potential of intelligent learning models to deliver lightweight, deployable, and effective intrusion detection for IoT systems utilizing MQTT and MQTT-SN communication protocols.
Empowering energy management: anomaly detection in smart meter data for proactive consumption control Batchalakuri Jyothi; Bhavana Pabbuleti; Beeravalli Mounika; Hrushitha Kalapala; Meda Uma Santhosh Chandra; Sanaboina Sai Srilakshmi; Bommasani Ganesh Babu; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Rami Reddy Chilakala
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10957

Abstract

The increasing deployment of smart energy meters (SEMs) has enabled real-time monitoring of energy consumption, but the vast data generated makes it challenging to detect anomalies that may indicate inefficiencies, faults, or unauthorized usage. This study aims to enhance energy management by developing a hybrid anomaly detection framework that improves accuracy while providing actionable insights for consumers. The proposed method integrates statistical and machine learning (ML) approaches, specifically Z-score, local outlier factor (LOF), one-class support vector machine (SVM), and isolation forest (iForest), to analyze simulated smart meter data. An anomaly is flagged only when identified by all four methods, thereby reducing false positives and improving reliability. The framework is implemented in an interactive dashboard built with streamlit, offering real-time visualization, peak-time alerts, usage forecasts, and personalized consumption suggestions. Results demonstrate that the hybrid approach outperforms single-method models, achieving higher detection accuracy and practical applicability. The findings highlight the potential of combining complementary detection techniques with proactive feedback to empower consumers, reduce energy wastage, and support sustainable energy management. This work provides a scalable foundation for future real-time deployment in smart grids and microgrid environments.
A novel hybrid model for brain tumor classification leveraging U-Net segmentation and ResNet50 architecture Nattavut Sriwiboon; Songgrod Phimphisan
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.9855

Abstract

Brain tumors are life-threatening conditions requiring accurate and timely diagnosis for effective treatment. This paper proposes a novel hybrid model combining U-Net for tumor segmentation and residual network 50 (ResNet50) architecture for classification to achieve performance in brain tumor classification from magnetic resonance imaging (MRI) images. This paper proposes a novel hybrid model that integrates U-Net for tumor segmentation with ResNet50 architecture for classification, enabling robust multi-class classification across glioma, meningioma, pituitary tumor, and no tumor classes. Utilizing a diverse dataset of 7,023 MRI images, the model achieves a remarkable accuracy of 99.78±0.05%, outperforming existing methods. Compared to related works, the proposed model demonstrates superior accuracy and scalability. This hybrid approach addresses key challenges in medical imaging, providing a robust and interpretable solution for real-world clinical applications.
Simulation and optimization of inverse kinematics algorithms for real-time target tracking in inertial stabilization platforms Abderahman Kriouile; Soufiane Hamida; Abdoul Latif Abdou Moussa
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10921

Abstract

Two-axis gimbal systems are engineered to maintain visual lock on target objects by actively counterbalancing movements, regardless of whether these originate from the target or the mounting platform. This research investigates the creation and refinement of inverse kinematics (IK) algorithms for achieving accurate, instantaneous target tracking in inertial stabilization platforms (ISPs), commonly referred to as gimbal systems. Such platforms are essential in applications requiring exceptional stability and precision, including surveillance operations, navigational systems, and scientific investigations. The study commences with a comprehensive analysis of the mathematical foundations underlying IK, with particular attention to the challenges posed by real-time processing requirements. To address these obstacles, sophisticated optimization methods are employed, with an emphasis on reducing computational latency and improving tracking accuracy. The developed algorithms are tested within a Simscape Multibody simulation environment, enabling thorough evaluation across various operational scenarios. Validation incorporates both simulated conditions and practical field tests to confirm the algorithms' durability and functional effectiveness. Results demonstrate significant improvements in both tracking precision and system reactivity, providing a foundation for more efficient and reliable gimbal systems in challenging dynamic environments.
Leveraging defense-in-depth through a deception-driven security model for smart university Naagas, Marlon A.; Gamilla, Anazel P.; Rabang, Mary Camille D.
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11299

Abstract

Cybersecurity remains one of the biggest challenges to address as the education sector shifts to the smart university concept. The education sector has experienced in recent years a noticeable rise in cyberattacks, revealing limitations in relying solely on traditional defense-in-depth (DID) security strategies. In response, the study implements a deception-driven security model (D-DSM) designed for a campus network environment. The proposed model incorporates decoy resources managed through a centralized deception mechanism to mislead attackers, divert malicious activities away from critical assets, and provide meaningful indicators of attack behavior, resulting in a more effective way to mitigate attacks. Rather than replacing existing defenses, the model complements current security controls by improving the visibility of advanced and lateral attacks while helping reduce false alerts. Adding deception as an active security layer is a useful way to make networks more resilient and helps build smarter, safer, and more sustainable university infrastructures.
Exact outage and intercept analysis of physical layer security in half-duplex energy harvesting relay systems Ngo, The-Anh; Tran, Xuan-Truong; Le, Viet-Thanh; Nguyen, Thien P.; Ha, Duy-Hung
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10207

Abstract

This paper investigates physical layer security (PLS) in a half duplex (HD) relay network with radio frequency energy harvesting (EH). We consider an amplify and forward (AF) protocol in which both the source and the relay harvest energy from a dedicated power beacon, while a passive eavesdropper attempts to intercept the confidential message. Unlike prior studies that rely on asymptotic analysis or numerical integration, we derive exact closed form expressions for the outage probability (OP) and the intercept probability (IP). The proposed expressions characterize the reliability and secrecy trade-off in energy limited cooperative relaying and enable efficient performance evaluation. Monte Carlo simulations validate the analysis and match the theoretical results. Moreover, the closed form results reveal how channel conditions and harvesting duration jointly shape outage and interception behaviors. We further quantify the impacts of normalized transmit power, the time allocation factor between harvesting and information transmission, and the EH efficiency. The results provide practical guidance for selecting system parameters to meet target reliability and secrecy levels, and they show that the time allocation factor must be carefully chosen to balance harvested energy and transmission time.

Page 301 of 305 | Total Record : 3049

 299   300   301   302   303 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 3: June 2026 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