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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 6,345 Documents
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Cloud internet of things-based cyber-physical system for microalgae integrated-aquaculture recirculating system in Sarawak Kee, Keh-Kim; Lau, John Sie Yon; Yong, Alan Huong Ting
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1030-1038

Abstract

The escalating demand for high-quality protein has driven commercial aquaculture's growth, and microalgal biomass shows potential to support this sector and contribute to global food security. Digitalizing integrated microalgal-aquaculture systems can significantly enhance sustainable protein production. Enabling technologies like the internet of things (IoT) and cyber-physical systems (CPS) are crucial for creating resilient aquaculture systems that ensure profitability, ecosystem health, and climate adaptability. However, applying cloud IoT and CPS solutions in the microalgae industry, especially the integrated microalgae and prawn farms remain underexplored. This work aims to develop a smart system for real-time monitoring and analysis of integrated microalgae and prawn farms in Sarawak, supported by an intelligent decision-support system. Utilizing a hybrid cloud-fog architecture, the system ensures efficient data acquisition, storage, and analysis and provides real-time monitoring through various user interfaces. Deployed in the plant site for over three months, the proposed system has proven effective in enhancing process efficiency and functionality, offering valuable reference in sustainable aquaculture for future enhancements such as multi-sensor and multi-site deployment in other farming systems to promote holistic environment sustainability and digital transformation.
An energy-optimized A* algorithm for path planning of autonomous underwater vehicles in dynamic flow fields Tiep, Do Khac; Tien, Nguyen Van; Thanh, Cao Duc
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp753-765

Abstract

This paper presents the development and implementation of an energy-optimized A* algorithm for autonomous underwater vehicle (AUV) path planning in these complex environments. The core of the approach is the integration of a computationally efficient flow field model and a detailed AUV energy consumption model directly into the A* search heuristic. The energy model considers factors such as drag forces, relative velocity between the AUV and the flow, and AUV maneuvering. The A* cost function is modified to prioritize paths that minimize the predicted total energy expenditure, while simultaneously ensuring obstacle avoidance and path feasibility. The algorithm was implemented and validated using a simulated environment with varying flow conditions. Results demonstrate that the proposed energy-optimized A* algorithm achieves a significant reduction in energy consumption – up to 50% in tested scenarios – compared to a standard A* implementation, while successfully generating collision-free and dynamically feasible paths. This work contributes a practical and effective solution for energy-aware AUV navigation in dynamic underwater environments, enabling longer mission durations and improved operational efficiency.
Dynamic analysis of a human-transporting robot climbing stairs Dat, Duong Tan; Ky, Le Hong; Thuan, Tran Duc
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp638-650

Abstract

Robots used for transporting people on stairs face several limitations regarding tipping and safety hazards. Changes in the robot's center of gravity during stair climbing can generate tipping moments, leading to instability, tipping, and increased danger to users. This paper presents the modeling and analysis results of a tracked robot for transporting people on stairs, equipped with an anti-tipping mechanism based on center of gravity balance, combined with a vibration-damping mechanism mounted at the rear of the robot to enhance stability during stair climbing. Based on Newton-Euler's formulas, robot dynamics equations are established to describe the motion and analyze the robot's stability characteristics. Simulation and experimental results investigating the changes in center of gravity, velocity, tipping moment, and balancing moment of the robot during uphill and downhill movement were performed using MATLAB Simulink software. Simulation results indicate that the robot's center of gravity is adjusted and stabilized throughout both uphill and downhill movements. Practical experiments conducted on a fabricated robot model, capable of carrying a 100 kg load and moving up and down stairs with a 35-degree incline, demonstrated the feasibility and effectiveness of the proposed mechanical design. The results showed good agreement in kinematic trends between experimental and simulated data during the stair climbing, stair-on, and stair-step transition phases. This agreement between experimental and simulation results proved the correctness of the robot system and the constructed dynamic model. The research results provide a basis for developing control algorithms for robots that efficiently transport people up and down stairs in buildings.
Parametric analysis to optimize a tradeoff between the efficiency and demagnetization of line-start permanent magnet synchronous motors Tuan, Le Anh; Thuy, Trinh Bien; Y., Do Nhu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp563-576

Abstract

The line-start permanent magnet synchronous motors (LSPMSMs) have many advantages, such as high efficiency and power factor, high energy density, and the ability to line-start. Therefore, the LSPMSMs are being studied to partially replace the induction motors (IMs) currently in use. However, LSPMSMs have disadvantages, including poor starting capability, and the permanent magnets may experience irreversible demagnetization during operation. Thus, this paper uses parametric analysis method to analyze the size of the permanent magnets to optimize the efficiency of the motor while ensuring that the permanent magnets do not undergo irreversible demagnetization. A 15 kW, 2-pole LSPMSM was used for experimentation, and the results show that the motor achieves the highest efficiency of ηmax = 95.5% at wM = 35 mm. However, when the motor thickness wM is greater than or equal to 34 mm, the motor experiences significant demagnetization. Thus, selecting permanent magnets (PM) size and material type that balance motor efficiency and avoid irreversible demagnetization needs careful consideration. Additionally, the experimental and simulation results are consistent, confirming the accuracy between the two methods.
Architectural trade-offs: comparative analysis across K3s, serverless, and traditional server deployments P., Prajwal; Teli, Naveen B.; H. N., Nishal; Dey, Nimisha; Deenadhayalan, Pratiba; Pattar, Ramakanth Kumar; Hadagali, Pavithra; P. R., Skanda
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp873-882

Abstract

In modern software architecture, combining serverless computing, microservices, and containers improves scalability, performance, observability, and resilience. However, choosing the right deployment strategy is crucial. Current individual deployment methods often limit productivity because of poor integration options. This study looks at three deployment approaches: Kubernetes cluster, AWS Lambda (serverless), and Traditional Java Server. We tested performance under different workloads using virtual machines and simulations. The results show that the K3s cluster provides high throughput and low latency because it manages resources directly. AWS Lambda’s pay-as-you-go model, along with its built-in cost optimization, works well for event-driven workloads. In contrast, Java Microservice is cost-effective but needs manual tuning to control latency and error rates. Bringing these scenarios together into a single service mesh architecture could help optimize costs, performance, and system resilience.
Enhancing ride-hailing adoption: understanding factors influencing ride-hailing user attitudes and reuse intention Mudjahidin, Mudjahidin; Athallah, Rafid Ikbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp905-913

Abstract

Ride-hailing applications (RHA) have emerged as a revolutionary force in the transportation landscape, offering convenient and on-demand mobility solutions, thus gaining widespread popularity in the transportation sector. However, concerns arise as many RHA startups find it difficult to survive in Indonesia, and even big RHA startups are still at risk. RHA must preserve user reuse intent in order to ensure service continuation. Based on the innovation diffusion theory (IDT), the unified theory of acceptance and use of technology (UTAUT), and additional factors, this study examines 11 variables and their impact on consumer attitudes and reuse intention in a model of ride-hailing service adoption. An online survey was utilized to gather data from various demographic backgrounds, and managed to gather data from 240 respondents. Analysis was conducted using partial least squares structural equation modeling (PLS-SEM) to assess the correlations between the variables. The findings revealed that perceived usefulness, perceived ease of use, perceived risk, compatibility, and personal innovation significantly influenced consumer attitudes. Additionally, it was shown that the attitude variable and customer reuse intention were positively and significantly correlated. Based on this outcome, recommendations were made to RHA providers to improve user attitudes and intentions to reuse.
Integrated deep learning approach for real-time object detection and color analysis Byrappa, Srinivas Dibbur; Gajendra, Kushal; Puttaraju, Rohith Holenarasipura; Malini, Tumakalahalli Nagaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp863-872

Abstract

Object identification is one of the major application areas of deep learning that provides significantly better feature extraction and representation than more conventional methods of recognition. Driven by the growing significance of conjunction of objects detection and color interpretation in contemporary computer vision systems, the current work proposes an integrated, real-time deep learning system that completes the task of object localization and color analysis. It is suggested that the proposed system employs a faster region-based convolutional neural network (Faster R-CNN) with backbone of ResNet-50 and supplemented with a feature pyramid network to perform multi-scale feature aggregation. The model was trained and tested using the Pascal VOC 2012 dataset and it showed good results with the average precision of 0.8114, F1 of 0.6232 and IoU of 0.7096. The large set of experiments on different learning rates and training epochs allowed optimizing the detector to work well in a variety of conditions. To enhance even more, visualization histogram of oriented gradients (HOG) and gradient-weighted class activation mapping (Grad-CAM) was used to gain a more profound understanding of the significance of features and the logic behind a model. This study complements image perception with color by combining object recognition and color in a single architecture, which can result in fruitful applications in areas of autonomous vehicles, industrial automation, and medical imaging.
An interpretable deep learning framework for early detection of depression using hybrid architectures Venkateshagowda, Chaithra Indavara; Ranganathasharma, Roopashree Hejjajji; Chandrashekaraiah, Yogeesh Ambalagere
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp895-904

Abstract

Current techniques for detecting depression are labor-intensive and subjective, depending on clinical interviews or self-reports. There is a growing adoption of machine learning (ML) and natural language processing (NLP) to automatically identify depression in textual data. The lack of interpretability, which is essential for healthcare applications, is still a major obstacle, though. By combining convolution neural network (CNN) for feature extraction, bidirectional long short-term memory (BiLSTM) for capturing sequential dependencies, and transformer-based pre-trained language model (PTLM) for contextual understanding, this study offers an interpretable framework for early depression identification. Additionally, the system uses a novel interpretability method to guarantee transparent decision-making. The outcome of the proposed system is found to achieve 96.2% accuracy, 94.5% precision, 95.1% recall, and 94.8% F1-score, which is a significant improvement over current method. This framework acts as a useful tool for early mental health intervention.
Intelligent systems, AI/ML, IoT, smart grids, robotics, healthcare, and emerging innovations Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp559-562

Abstract

This editorial discusses the latest trends and emerging ideas in electrical and computer engineering. It emphasises how intelligent systems, artificial intelligence and machine learning (AI/ML), the Internet of Things (IoT), smart grids, robotics, and healthcare technologies are transforming the field. The issue highlights the integration of data-driven intelligence, adaptive control, and real-time monitoring across various applications, including industrial automation, energy management, environmental monitoring, and personalised healthcare. Key themes encompass the development of AI/ML models for predictive analytics, IoT-enabled cyber-physical systems for autonomous decision-making, robotics for both human assistance and industrial operations, and smart grids aimed at achieving sustainable and resilient energy distribution. Furthermore, emerging innovations tackle challenges related to scalability, interpretability, energy efficiency, security, and the ethical deployment of intelligent technologies. By examining these interconnected domains, the editorial underscores the increasing interplay between computational intelligence, connected systems, and societal needs, while offering suggestions for future research directions and considering the potential impact of these technologies on global industries and human well-being.
Identification of critical buses in the Sulbagsel electrical system network integrated with wind power plants Ilyas, Andi Muhammad; Siswanto, Agus; Rahman, Muhammad Natsir
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp587-597

Abstract

The growing deployment of renewable energy has become increasingly important as conventional fossil-based generation faces sustainability and resource limitations. On Sulawesi Island, Indonesia, wind energy contributes to the regional grid through several wind power plants, whose fluctuating generation introduces operational concerns for system stability. This study investigates the stability performance of the Sulbagsel 78-bus network by pinpointing vulnerable buses and examining the effects of wind power variability. A hybrid stability index (HSI), which integrates multiple stability indicators, is applied to obtain a more robust assessment. The analysis shows that the entire system operates within a secure margin, with all index values remaining below the critical limit (<1). The most sensitive areas are located on the transmission paths connecting Bus 56 Sidera–Bus 57 Sidera 70 kV (0.02268), Bus 38 Bosowa–Bus 40 Pangkep (0.02220), and Bus 73 Powatu 150 kV–Bus 74 Powatu 70 kV (0.02187). In contrast, the Bus 24 Tanjung Bunga–Bus 25 Bontoala corridor demonstrates the strongest stability margin (0.00026). These results indicate that the variability of wind generation does not impose significant negative impacts on the overall stability of the Sulbagsel power system.

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