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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 88 Documents
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Search results for , issue "Vol 15, No 6: December 2025" : 88 Documents clear
Optimization of water resource management in crops using satellite technology and artificial intelligence techniques Reyes-Galván, Erick Salvador; Bolivar-Gomez, Fredy Alexander; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5847-5853

Abstract

This study aims to optimize water consumption in avocado crops through the application of satellite technology, machine learning algorithms, and precise climate data from the climate hazards group infrared precipitation with stations (CHIRPS) system. Crop classification in satellite images is conducted using the random forest algorithm, enabling detailed categorization of cultivated areas, urban land, soil, and vegetation, with a specific focus on avocados due to their high-water demand. Given its economic importance and status as one of the most water-intensive crops, avocado cultivation presents a critical challenge for agricultural sustainability. To validate predictive models and ensure classification accuracy, advanced evaluation methodologies such as the confusion matrix and Cohen's kappa index are utilized, quantifying the precision and reliability of the results. This estimation of water consumption under deficit and surplus conditions offers key insights for efficient water management in avocado cultivation. The results generated can enhance agricultural efficiency by aligning water use with the crop’s actual requirements, thereby contributing to the reduction of its water footprint.
Designing, developing and analyzing of a rectangular-shaped patch antenna at 3.5 GHz for 5G applications at S band Halder, Sukanto; Rana, Md. Sohel; Ahad, Md Abdul; Shahriar, Md. Shehab Uddin; Al Mamun, Md. Abdulla; Rahaman, Md. Mominur; Faruk, Omer; Ahmed, Md. Eftiar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5422-5432

Abstract

This research study focuses on the design and analysis of two distinct patch antennas for 5G applications at 3.5 GHz. Rogers RT5880 served as the foundational material for antenna designs I and II. A 50 Ω feed line is utilized to supply both antennas. According to the calculations, Design I exhibits a reflection coefficient (S11) of -32.98 dB, a voltage standing wave ratio of 1.045, a gain of 7.81 dBi, an efficiency of 89.2%, and a surface current of 66.82 A/V. Design II has a reflection coefficient (S11) of 34.98 dB, voltage standing wave ratio (VSWR) of 1.036, gain of 8.78 dBi, efficiency of 89.87%, and surface current of 62.7 A/V. Among the two antenna designs, design II outperformed design I, and the results indicate that the antenna fulfilled the designated purpose. The novelties of the proposed paper are to design two different patch antennas using same materials and highlight the performance of the design parameters. Design II is proficient in supporting 5G services owing to its advantageous performance. In addition, S11 of the antenna is reduced to bring the VSWR value is close to 1. Also, improve gain, directivity and efficiency by bringing the antenna impedance matching close to 50 Ω.
Convolutional neural network-based hybrid beamforming design based on energy efficiency for mmWave M-MIMO systems Ayad, Hanane; Bendimerad, Mohammed Yassine; Bendimerad, Fethi Tarik
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5443-5452

Abstract

Millimeter-wave (mmWave) massive multiple-input multiple-output (M- MIMO) technology brings significant improvements in data transmission rates for communication systems. A key to the design of mmWave M-MIMO systems is beamforming techniques, which focus signals toward specific directions but rely on expensive, energy-intensive radio frequency (RF) chains. To address this issue, hybrid beamformers (HB) have been introduced as a partial solution, and deep learning (DL) has proven effective for HB design. However, previous works utilizing machine learning (ML) networks have primarily focused on the spectral efficiency (SE) metric for constructing HB. In this paper, we present a convolutional neural network (CNN) architecture whose loss function is defined to maximize energy efficiency (EE) directly. The network jointly learns analog and digital beamformers by evaluating EE (throughput per total power, including phase shifters, switches, digital-to-analog converters (DACs), and RF chains) and selecting the configuration that yields the highest EE. The CNN takes a channel matrix as input and outputs RF and baseband beamformer matrices. Simulation results validate the effectiveness of the proposed M-MIMO EE scheme, achieving significant EE improvements by optimizing hybrid precoding and reducing RF chain usage.
Enhancing system integrity with Merkle tree: efficient hybrid cryptography using RSA and AES in hash chain systems Fauzi, Irza Nur; Farikhin, Farikhin; Jie, Ferry
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5679-5689

Abstract

An analysis is conducted to address the growing threats of data theft and unauthorized manipulation in digital transactions by integrating \structures within hash chain systems using hybrid cryptography techniques, specifically Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES) algorithms. This approach leverages AES for efficient symmetric data encryption and RSA for secure key exchanges, while the hash chain framework ensures that each data block is cryptographically linked to its predecessor, reinforcing system integrity. The Merkle tree structure plays a crucial role by allowing precise and rapid detection of unauthorized data changes. Empirical analyses demonstrate notable improvements in both the efficiency of cryptographic processes and the robustness of data validation, underscoring the method’s applicability in high data throughput environments such as educational institutions. This research makes a substantive contribution to information security by offering a sophisticated solution that strengthens data protection practices, ensuring greater resilience against increasingly sophisticated data threats.
Evaluating clustering algorithms with integrated electric vehicle chargers for demand-side management Abida, Ayoub; Majdoul, Redouane; Zegrari, Mourad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5837-5846

Abstract

The integration of electric vehicles (EVs) and their effects on power grids pose several challenges for distribution operators. These challenges are due to uncertain and difficult-to-predict loads. Every electric vehicle charger (EVC) has its specific pattern. This challenge can be addressed by clustering methods to determine EVC energy consumption clusters. Demand side management (DSM) is an effective solution to manage the incoming load of EVs and the large number of EVCs. Considering the challenges of peak consumptions and valleys, the adoption of vehicle-to-grid (V2G) technology requires mastering load clusters to develop energy management systems for distributors. This work used clustering algorithms (K-means, DBSCAN, C-means, BIRCH, Mean-Shift, OPTICS) to identify load curve patterns, and for performance evaluation of algorithms, it worked on metrics like the Silhouette coefficient, Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) to evaluate results. C-means achieves the best overall clustering performance, evidenced by the highest Silhouette coefficient (0.30) and a strong Calinski-Harabasz score (543). Mean-Shift excels in the Davies-Bouldin Index (1.13) but underperforms on other metrics. BIRCH provides a balanced approach, delivering moderate results across evaluated metrics.
Design and development of home-grown biometric fingerprint device and software for attendance and access control Soyemi, Jumoke; Ige, Ogunyinka Olawale; Soyemi, Olugbenga Babajide; Ademola, Ajibodu Franklin; Jayeoba, Adaramola Ojo; Olumide, Afolayan Andrew; Amode, Habeeb O.; Akinde, Mukail Aremu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5616-5632

Abstract

This study details the design, development, and deployment of an Android-based Biometric Fingerprint system tailored for institutional access control, attendance tracking, exam monitoring, and staff management. Developed collaboratively by the Innovation Centre and departments across engineering and information and communication technology (ICT), the system integrates custom hardware and software. Hardware includes fingerprint sensors connected to an ATMEGA8 microcontroller and Android interfaces for portability. The software uses modular architecture, comprising a Kotlin-based mobile app with Jetpack Compose, a Laravel-powered web admin panel, and a secure backend API hosted on a virtual private server (VPS). Fingerprint data is safely stored using base64 encoding, enabling accurate user authentication and real-time tracking. A functional prototype was built, tested, and refined, with 95 units deployed in a pilot phase. The system supports multiple fingerprint profiles, secure data handling, and integration with existing institutional platforms. Emphasizing customization, modularity, and adherence to ICT policies, the research also serves as a training tool for staff and students, enhancing operational efficiency and supporting local technology development. Performance evaluation showed a FAR of 0.5%, FRR of 1.2%, and an average authentication time of 2.3 seconds. Post-deployment, student attendance increased by 15%, fee compliance by 10%, and 89% of users rated the system as easy to use. This work demonstrates effective hardware-software co-design for scalable biometric authentication in educational settings.
Detection of breast cancer with ensemble learning using magnetic resonance imaging Nadkarni, Swati; Noronha, Kevin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5371-5379

Abstract

Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
H-shaped terahertz patch antenna with metamaterials for biomedical applications Saidi Alaoui, Kaoutar; Younes, Siraj; Jaouad, Foshi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5215-5222

Abstract

This paper presents the design and simulation of an H-shaped terahertz microstrip patch antenna integrated with a metamaterial (MTM) layer to enhance performance for biomedical sensing applications. The antenna modeled using high frequency structure simulator (HFSS), is optimized for 4.37 THz operation. While FR4 is used in simulations for baseline analysis, alternative low-loss substrates such as polyimide or quartz are recommended for practical THz applications. The antenna design uses an FR4 substrate with a dielectric constant of 4.4 and a thickness of 2 μm. Ground plane, feed line, and patch are made of copper material. The integration of the MTM enhance clearly the antenna characteristics. This integration helps to improve the antenna impedance matching; the reflection coefficients was enhanced from -25.01 to -63.10 dB. Additionally, this integration boost also the antenna radiation characteristics, increasing the gain from 2.62 to 3.86 dB and the directivity from 3.57 to 4.97 dB.
Greenhouse gas reduction system for engines using electrolyte technology Chainok, Bopit; Wasuri, Boonthong; Chainok, Piyamas
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5524-5534

Abstract

This research focuses on developing a system to reduce greenhouse gas emissions in internal combustion vehicle engines using electrolyte technology and embedded programming on an electronic board via the OBI protocol. The main objectives are to create a prototype, apply it in real-world scenarios, evaluate its efficiency, and facilitate technology transfer. The system, designed to reduce greenhouse gases from vehicles, consists of a Bluetooth on-board diagnostics (OBD) scanner connected to the electronic control unit (ECU). This scanner transmits data to an embedded microcontroller through a Bluetooth module. The microcontroller, which includes software for controlling oxygen measurement and production, operates to decrease greenhouse gas emissions. The results show that the electronic device, IC ELM327, decodes OBD into RS232, processes the oxygen output from the exhaust pipe using embedded programming on the Arduino Uno-R3 microprocessor, and controls the oxygen production unit with electrolyte technology. The system adds 9.82% oxygen to the exhaust and reduces carbon monoxide by 21.04% and carbon dioxide by 13.86%. Additionally, the technology transfer received high satisfaction with a mean score of 4.61, indicating efficient technology dissemination.
Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning Herbaz, Nourdine; El Idrissi, Hassan; Sabir, Hamza; Badri, Abdelmajid
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5162-5172

Abstract

Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.

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