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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 6,301 Documents
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Trends of unmanned aerial vehicles in smart farming: a bibliometric analysis Kgopa, Alfred Thaga; Manyela, Sikhosonke; Monchusi, Bessie Baakanyang
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.pp5746-5758

Abstract

This paper presents a review of the trends of unmanned aerial vehicles (UAV) in agriculture using a bibliometric analysis. This bibliometric analysis shows that 1676 articles were accessed from the Elsevier Scopus database between 2013 and 2023. Our findings indicate research related to UAVs in agriculture has surged over the years, but the adoption and acceptance of smart farming technology in sub-Saharan Africa remains inert. This study employed VosViewer in data analysis and bibliometrics. Our findings show that China leads all countries and followed by the United States on UAV publications in smart farming research foci. Our findings indicate that UAVs are impactful in improving crop growth, crop health monitoring, and may be beneficial to small-holder farmers with increased yields. We recommend that sub-Saharan Africa nations accelerate collaboration with China and United States in advancing climate smart agriculture practices to mitigate food insecurity risks.
Bibliometric analysis to highlight the impacts of digitalization, artificial intelligence, and modern optimization on the human environment during international armed conflicts Rezk, Hegazy; Mahmoud, Montaser
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.pp5815-5826

Abstract

This research is conducted to explore the impacts of digitalization and artificial intelligence (AI) on the human environment during international armed conflicts, aiming to identify trends, challenges, and potential solutions to improve humanitarian aid, decision-making, and conflict resolution strategies. To identify the main research issues about the effects of AI and digitalization on the human environment in conflict situations, this work employs a bibliometric analysis. A bibliometric analysis of the impacts of digitalization and artificial intelligence on the human environment during armed conflicts by examining 544 selected papers from Scopus database has been conducted. Knowledge mapping techniques involving collaboration analysis, co-citation analysis, and keywords co-occurrence analysis methods are adopted in bibliometric analysis. Based on a comprehensive analysis of literature, this work attempts to pinpoint the key areas of interest, knowledge gaps, and new problems in this domain. The findings of this bibliometric analysis contribute to a better understanding of the complex interactions between technology, armed conflicts, and the human environment, with implications for humanitarian action, international law, and conflict resolution efforts. The bibliometric analysis reveals that the United States of America (USA) is by far the leading country in research within this field, with a substantial frequency of 181 documents. It significantly surpasses that of other countries, indicating its dominant position in the research landscape. In sum, the work offers suggestions for further research and policy intervention.
Hybrid CNBLA architecture for accurate earthquake magnitude forecasting Shams, Somia A.; Mohamed, Asmaa; Desuky, Abeer S.; A. Elsharawy, Gaber; El-Sayed, Rania Salah
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.pp5879-5893

Abstract

Earthquake prediction in seismology is challenging due to sudden events and lack of warnings, requiring rapid detection and accurate parameter estimation for real-time applications. This study proposed a novel automatic earthquake detection model to enhance the processing and analysis of seismic data. The hybrid model comprises convolutional layers, normalization techniques, bidirectional long short-term memory (Bi-LSTM) networks, and attention mechanisms, collectively referred to as the hybrid convolutional–normalization–BiLSTM–attention (CNBLA) model. The attention mechanism allows the model to focus on critical segments of seismic sequences, while layer normalization stabilizes training by normalizing activations, thus reducing the effects of input scale variations. This dual approach mitigates the impact of input scale variations and enhances the model’s ability to effectively decode complex temporal patterns. The hybrid CNBLA model optimizes the extraction and processing of temporal features from raw waveforms recorded at single stations, thereby improving the accuracy and efficiency of seismic magnitude estimation. The proposed model is evaluated using two datasets: the STEAD and USGS achieving a mean square error (MSE) values 0.054 and 0.0843 and a mean absolute error (MAE) 0.15 and 0.2526 respectively. The hybrid CNBLA model outperforms two baseline models and five state-of-the-art approaches in earthquake magnitude estimation, improving seismic monitoring and early warning systems.
Augmented reality for ancient attractions Trakulmaykee, Numtip; Janpetch, Katchaphon; Ladawong, Patchanee; Khamouam, Atitaya
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.pp5717-5727

Abstract

The study focuses on augmented reality (AR) understanding, development and evaluation. For evaluation, this paper assesses the role of multimedia types in perceived enjoyment, and investing in how perceived usefulness, ease-of-use, and enjoyment affect the adoption of AR by tourists. A quantitative approach was employed to collect data from 115 participants who experienced an AR application designed for 14 ancient attractions in Songkhla, Thailand. The multimedia content included 3D models, historical videos, drone videos, billboard navigations, and text animations. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that perceived ease-of-use and enjoyment significantly influence behavioral intention (BI) as significant factors at 0.01, while perceived usefulness did not affect BI in the context of ancient attractions. Moreover, the multimedia types directly impacted the perceived enjoyment at a significant level of 0.05, and indirectly impacted BI. This study contributes to the theoretical understanding of AR adoption in tourism by integrating multimedia types with tourist perceptions and BI. Practically, it provides insights for designing AR applications that enhance visitor engagement and satisfaction in heritage tourism.
Enhanced ankle physiotherapy robot with electromyography - triggered ankle velocity control Adiputra, Dimas; Nismara, Radithya Anjar; Lubis, Muhammad Rafli Ramadhan; Rizkianingtyas, Nur Aliffah; Satrio, Kensora Bintang Panji; Arif, Rangga Roospratama; Salsabila, Annisa
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.pp5314-5326

Abstract

Previous ankle physiotherapy robots, called picobot rely on predefined trajectories continuous passive movement without considering patient intent, limiting the encouragement of user-intent motion. This study then integrates electromyography (EMG) signals as triggers into picobot with an ankle velocity-based control system. The upgraded robot activates movement in specific gait phases based on muscle activity, synchronizing therapy with the patient’s intent. Functionality test on 7 young male healthy subjects investigates leg muscles, such as Tibialis Anterior, Soleus, and Gastrocnemius muscles for the most significantly contribute to ankle movements. Then, the muscle is tested to trigger picobot movements. Functionality tests revealed the Tibialis muscle significantly contributes to gait phases 2, the Soleus is prominent in phases 3 and 4, and gastrocnemius is active on phase 1. The robot successfully performs plantarflexion when EMG signals exceed a 1.58 V threshold, reaching a target position of -0.11 rad at a constant velocity of -0.62 rad/s. These findings establish a foundation for future trials since patient testing has not yet been conducted. By promoting active participation, this innovation has the potential to enhance rehabilitation outcomes. Incorporating user-intent triggers may accelerate recovery and improve healthcare accessibility in Indonesia, offering a significant advancement in physiotherapy technologies.
Exploring feature engineering and explainable AI for phishing website detection: a systematic literature review Alsuqayh, Norah; Mirza, Abdulrahman; Alhogail, Areej
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.pp5863-5878

Abstract

Detecting phishing websites is a rapidly evolving field aimed at identifying and mitigating cyberattacks targeting individuals, organizations, and governments. Ongoing progress in artificial intelligence (AI) has the potential to revolutionize phishing detection by enhancing model accuracy and improving transparency through eXplainable AI (XAI). However, significant challenges remain, particularly in integrating feature engineering with XAI to address sophisticated phishing strategies including zero-day attacks, that evade traditional detection mechanisms. To overcome these challenges, this examines the impact of feature engineering and XAI in phishing detection, emphasizing their ability to enhance accuracy while providing interpretability. By integrating feature extraction with interpretable models, these techniques improve decision-making transparency and system robustness. This paper presents the first systematic literature review (SLR) focusing on the impact of feature engineering and XAI on state-of-the-art phishing detection approaches. Additionally, it identifies critical research gaps and challenges, including scalability issues, the evolution of phishing techniques, and balancing complexity with interpretability. The findings provide valuable academic insights while offering practical recommendations for developing accurate and interpretable phishing detection systems, aiding organizations in strengthening cybersecurity measures.
Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies Dogiparthi, Santhosh Gupta; K., Jayanthi; Pillai, Ajith Ananthakrishna; Nakkeeran, K.
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.pp5655-5666

Abstract

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Platforma: a modular and agile framework for simplified platformer game development Roedavan, Rickman; Leman, Abdullah Pirus; Pudjoatmodjo, Bambang
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.pp5535-5542

Abstract

Research on game development frameworks has been extensively conducted; however, most frameworks are still too general. Conventional game frameworks are challenging for students who are new to game development, especially with their limited information and skills. Beginner game developers should ideally be guided by a practical and specific framework to help them better understand the structure of game development in a more directed manner. This paper proposes platformer modular and agile framework (Platforma) that specifically designed for platformer game development. The framework is built based on the atomic design model, breaking down each minor feature of a platformer game element and grouping these features into more specific modules. The framework was tested on three teams of students. Each team was tasked with developing a platformer game with a minimum of 15 levels of the reach game goals typology. Testing results involving 100 respondents using the game experience questionnaire (GEQ) indicated that the games developed had a positive aspect score of 3.48 and a negative aspect score of 2.65. Overall, these results suggest that the Platforma can serve as an effective guide for beginners in developing platformer games.
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.

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