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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 3,049 Documents
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Feature extraction and machine learning methods for biometric recognition based on fusion of ECG and fingerprint Hafiz Ilhami; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Irwan Budiman; Andi Farmadi; Dwi Kartini; Puput Dani Prasetyo Adi; Jumadi Mabe Parenreng
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.v15i3.10541

Abstract

This research introduces a multimodal biometric authentication framework by amalgamating electrocardiogram (ECG) and fingerprint modalities through the utilization of diverse feature extraction methodologies and machine learning classifiers. The proposed methodology aspires to augment precision and mitigate spoofing vulnerabilities in contrast to traditional single-modality systems. Among the feature extraction techniques assessed—grayscale, binary, Sobel edge detection, and minutiae—Naïve Bayes (NB) in conjunction with minutiae features exhibited superior performance, attaining an accuracy rate of 96.25%. Supplementary experiments employing random forest (RF) and support vector machine (SVM) also revealed commendable classification efficacy, underscoring the robustness of the fusion methodology. This investigation provides a pragmatic and secure biometric framework by harnessing complementary biometric characteristics to enhance authentication dependability. The proposed system presents promising applications in real-world contexts, particularly concerning mobile security and healthcare access control. Future research endeavors will tackle challenges associated with ECG signal variability, computational efficiency, and extensive deployment.
An AI-enhanced hybrid project management model: a comparative evaluation using the weighted sum model Issam Talkam; Ibrahim Hamzane; Abdessamad Belangour
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.v15i3.10757

Abstract

Projects today face increasing complexity, uncertainty, and regulatory constraints that often limit the effectiveness of traditional and strictly Agile project management (PM) methods. Current hybrid methods remain largely theoretical and lack integrated decision-support tools that combine predictive analytics with transparent multi-criteria evaluation. To address this gap, this paper presents an artificial intelligence (AI)-enhanced hybrid project management model (AI–HPMM) that combines AI functionalities with the weighted sum model (WSM) to support adaptive, data-driven, and transparent decision-making. A systematic literature review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (2019–2025), identifying ten success factors aligned with project management body of knowledge (PMBOK) domains. These factors were weighted using WSM to compare Agile, Waterfall, and AI-enhanced hybrid methods. AI–HPMM achieved the highest WSM score (9.96/10), outperforming Agile (7.66/10) and Waterfall (7.90/10). The reviewed literature also reports a 15% reduction in schedule deviations, a 12–20% improvement in resource efficiency, and a 25–30% increase in decision-making speed. Overall, the proposed model improves adaptability, transparency, and responsiveness in complex and dynamic project environments.
Design and evaluation of a secure key exchange protocol using the Kyber-Dilithium algorithm Bambang Harjito; Muhammad Defaroyan; Fajar Muslim; Ery Permana Yudha; Endra Pratama
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.v15i3.11157

Abstract

Over 90% of the billions of people who use the internet globally use it through the transport layer security (TLS) protocol. TLS is a security standard that performs network authentication and data encryption when accessing the internet. Authenticated key exchange (AKE) is the protocol TLS uses for network authentication and key establishment during the TLS Handshake process. The AKE protocol utilizes a public key cryptosystem (PKC) and digital signatures with algorithms commonly used, namely elliptic curve cryptography (ECC) and Rivest-Shamir-Adleman (RSA). Future advancements in quantum computing may compromise the security of the widely used ECC and RSA algorithms. This research conducts an implementation and comparative analysis of post-quantum algorithms resistant to quantum computer attacks, specifically Kyber-Dilithium, in the context of the AKE protocol. The implementation is performed at three security levels: 128-bit, 192-bit, and 256-bit. The results show that the Kyber-Dilithium is greater than those of the RSA variant and much larger than those of the ECC variant. In contrast to the ECC and RSA variants, the Kyber-Dilithium algorithm variants perform better across all security levels, even if their byte sizes are greater.
Design of an educational platform based on augmented reality for the special education of children with dyscalculia Jhair Baltodano-Payahua; Sebastian Ramos-Cosi; Alicia Alva-Mantari; Gustavo Villar-Mayuntupa; Enrique Lee Huamani; Meyluz Paico-Campos; Laberiano Andrade-Arenas
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.v15i3.10754

Abstract

Dyscalculia is a learning disorder that affects between 3% and 8% of the world's population and is characterized by difficulty in acquiring basic mathematical concepts, mainly in children. In view of this, several technological tools have been explored that can help improve the education of these children, with augmented reality (AR) being a promising alternative. The objective of this research is to design an AR-based platform to support the special education of children with dyscalculia. An agile methodology, Scrum, was used for the development, which allowed for an interactive approach to the design of the prototypes. Consequently, these were evaluated and validated by six experts in the field. The result was an acceptance rate of 81.2%, demonstrating the viability of the prototype. It is concluded that the prototype using AR is an effective tool to support children with dyscalculia aged 8 to 10, as it allows for an interactive and beneficial learning experience to nurture their knowledge.
Territorial disparity index: a GIS-integrated data framework for territorial disparities measurement in rural Morocco Nassima Tabloul; Mohcine Kodad; Mimoun Boukhidous; El Houcine Addou
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.v15i3.10990

Abstract

Despite availability of numerous indicators for territorial evaluation and the measurement of their deprivation or advantage, the results remain confined to a coarse-grained spatial scale and cannot be applied at finer levels of territories like to Moroccan rural entities known as douars. This paper presents a bottom-up framework termed territorial disparity index (TDI) that quantitatively conceptualize territories as entities shaped through data-driven technological processes in order to better control and develop them and above all to reduce their multi-scale disparities. Through this study, we propose a geographic information system (GIS)-integrated data for composite index through two key dimensions: socio-economic and spatial-environmental aspects, quantified as scores based on 32 variables translated into indicators, then converted into decile ranks through a systematic data processing pipeline. The framework of TDI not only enhances spatial sensitivity often overlooked, but also provides a highly objective tool for mapping territorial disparity. Applied to actual data of a real Moroccan territory, in Berkane, this research introduces a novel methodology for territorial data collection, combining cross-sectional and spatial data analysis to digitally perceive these territories according to their levels of disadvantage. Principal component analysis (PCA) is then used to ensure robustness and objectivity of TDI index.
Comparative analysis of switching frequency in DTC-CBSVM and FOC of three-level NPC PMSM drive for EV applications Rakesh Shriwastava; Balajee Maram; Yogesh S. Pawar; Jyoti P. Rothe; Dinesh S. Wankhede; Hema Kale
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.v15i3.11565

Abstract

This paper presents a comparative analysis of switching frequency performance in direct torque control with carrier-based space vector modulation (DTC-CBSVM) and field-oriented control (FOC) of permanent magnet synchronous motor (PMSM) drives fed by a three-level neutral point clamped (NPC) inverter. PMSM drives are widely utilized in electric vehicles (EVs) due to their high efficiency, torque density, and reliability. However, the selection of an appropriate control strategy significantly influences inverter switching frequency, system efficiency, and overall drive performance. The DTC-CBSVM approach provides fast torque response and reduced torque ripple, whereas the FOC technique ensures precise control of flux and torque with smoother operation. In this study, both methods are implemented and evaluated under identical operating conditions. Simulation results highlight the trade-offs between dynamic response, switching frequency, and harmonic performance. The findings serve as a reference for selecting optimal control strategies for efficient PMSM-based NPC inverter drives in EV applications.
An open-source low-cost TDOA-based sound source localization system Mahmoud A. Alnaanah; Amir Abu Al-Aish; Mohd H.S. Alrashdan; Mohammad Zayed Ahmed; Haitham A. Alasha'ary; Hamzah Hmeidi
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.v15i3.10523

Abstract

Sound source localization (SSL) has many civilian and military applications, such as robotics, gunshot localization in nature reserves and warfare, surveillance, wild animal tracking, and rescue missions. One effective method of SSL is measuring the time difference of arrival (TDOA) for an array of microphones. The TDOA method requires designated hardware that is quite expensive and might require specific proprietary software; this makes SSL research expensive and difficult to develop software for, especially for hobbyists and educators. This paper presents a low-cost and easy-to-build SSL system that is built using 4 USB sound cards along with 4 microphones and a USB hub. The code for the system is made open for the public, and it is based on open-source applications, which are Linux, GStreamer, and Octave, to provide an open environment for studying and researching SSL. The localization process relies on finding the TDOA using generalized cross correlation (GCC) thresholding and finding the intersection point of three two-sheeted hyperboloids. The theoretical and experimental descriptions of the system are presented in this paper, along with some of the challenges, such as the microphones’ timing and position calibration. Despite the low sampling rate and high noise of the used USB sound cards, the system was able to locate the sounds within a 2.4 m radius with a root mean square error (RMSE) of 7.23% and a mean error of 6.12%.
Edge-iterated graph parameters: theory and applications to wireless sensor networks Kakkattumadathil Sreenivasan, Sreelatha; Kumar, Janardhanan Suresh; Thekkethuruthel Sadanandan, Indulekha; Karuppath, Narayanankutty
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.v15i3.11131

Abstract

In this paper, we study three graph parameters–the iterated chromatic number xn(G), the iterated domination number yn(G), and the iterated covering num- ber Bn(G)—through the line-graph transformations applied successively on a connected graph G. These edge-iterated parameters track how coloring, domination, and covering structures evolve as the graph undergoes successive linegraph transformations. For standard graph families such as paths, cycles, and grid graphs, we derive exact formulas and establish upper and lower bounds, revealing both periodic and divergent behaviours depending on graph structure. Since exact computation of these parameters becomes intractable for large networks, we propose a BFS-based greedy algorithm for estimating xn(G), and benchmark it against the Welsh–Powell and DSATUR algorithms. The simulation results validate the theoretical bounds and show that the proposed method is computationally efficient without significant loss in coloring quality. We further show that these parameters have natural interpretations in wireless sensor networks (WSNs): xn(G) informs frequency and time-slot assignment under multi-hop interference, yn(G) identifies minimal supervisory structures for fault tolerance, and Bn(G) guides energy-aware link monitoring. The framework thus connects iterated graph theory to concrete design problems in sensor network optimization.
Deep learning based identification of Crocidolomia pavonana larvae on mustard plants using Grad-CAM Susetianingtias, Diana Tri; Madenda, Sarifuddin; Risnawati, Risnawati; Maukar, Maukar; Patriya, Eka; Rodiah, Rodiah
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.v15i3.11343

Abstract

Mustard greens are an important vegetable commodity, but their production is often affected by pest attacks, especially the cabbage worm Crocidolomia pavonana (C. pavonana). The larvae damage leaf tissues and cause significant yield losses, while chemical control is often ineffective due to differences in insecticide sensitivity across larval instars. This study proposes a deep learning based classification approach combined with gradient weighted class activation mapping (Grad-CAM) to identify larval instars of C. pavonana on mustard plants. A dataset of 684 images covering instars 1 to 4 was collected from laboratory rearing and field observations, then processed using resizing and augmentation techniques and divided into training, validation, and testing sets with an 8 to 1 to 1 ratio. Two convolutional neural network (CNN) models, visual geometry group 19 (VGG19), and Xception, were implemented with additional fully connected layers. The VGG19 model achieved 94.20% accuracy and outperformed Xception. Grad-CAM successfully highlighted larval regions and supported visual interpretation. The results show that the proposed method can improve pest identification accuracy and support more effective pest management.
Metaheuristic nurse scheduling with hospital clustering using flower pollination algorithm Zuhanda, Muhammad Khahfi; Hartono, Hartono; Rahman, Sayuti; Gio, Prana Ugiana; Ongko, Erianto
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.v15i3.11243

Abstract

Effective nurse scheduling is essential to ensure balanced workloads, reduce fatigue, and maintain healthcare service quality. However, the nurse scheduling problem (NSP) is complex due to constraints related to nurse skills, task requirements, and legal working-hour limits. This study proposes an integrated framework combining a mathematical optimization model with metaheuristic algorithms to generate optimal daily nurse activity schedules. Genetic algorithm (GA) and simulated annealing (SA) are employed to produce near-optimal solutions for nurse populations ranging from 3 to 50 individuals, considering skill-level compatibility, workload balance, and maximum working hours. Experimental results using real scheduling data from 30 nurses across three skill levels demonstrate that all generated schedules satisfy the imposed constraints, with no nurse exceeding the 12hour daily working limit. Comparative analysis shows that GA achieves lower scheduling costs for larger nurse populations, while SA consistently requires significantly shorter computation times, making it suitable for time-sensitive applications. In addition, the flower pollination algorithm (FPA) is used to cluster 3,155 hospitals based on bed capacity, service variety, and workforce size, supporting data-driven workforce distribution analysis. The proposed framework integrates operational scheduling optimization with hospital-level clustering, providing practical decision support for healthcare workforce planning.

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