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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 45 Documents
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Search results for , issue "Vol 16, No 1: February 2026" : 45 Documents clear
Generalization of reactive power definition for periodical waveforms Kosobudzki, Grzegorz; Ładniak, Leszek
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp102-110

Abstract

The article presents a selection of reactive power definitions, which are applicable for implementation in energy meters. For sinusoidal current and voltage waveforms, all provided dependencies yield equivalent reactive power values. However, in the presence of distorted current and voltage, the power values are determined by the applied method (algorithm). Standardization requirements for reactive energy meters stipulate metrological verification under sinusoidal conditions. The selection of an optimal reactive power definition remains a problematic and ongoing subject of debate within the field. The paper shows that a generalized unique definition of additive reactive power derives from the definition of active power. Unlike active power, reactive power must be independent of the conversion of electric energy into work and heat. This independence is achieved if one of the waveforms – the current in the scalar voltage and current product (specifying active power) – is replaced by a special orthogonal waveform. An orthogonal waveform can be derived through either differentiation or integration. Reactive power obtained by this method is an additive within the system. When differentiation is employed, the reactive power for a nonlinear resistive load with a unique, time-invariant current-voltage characteristic will be zero. Some other properties of reactive power defined in this way are presented. This method is straightforward to implement in reactive energy meters.
Image classification using two neural networks and activation functions: a case study on fish species Hutapea, Oppir; Gaol, Ford Lumban; Matsuo, Tokuro
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp383-394

Abstract

Lake Toba is utilized for aquaculture fishing as a clear example of how this technology can be applied. One of the species presents is the red devil fish (Amphilophus labiatus), which is known to have started appearing in the last 10 years. This species is known to be very aggressive and damage the ecosystem. When their populations go unchecked, red-devils can cause a decline in local fish populations, potentially destroying the balance of the food chain in those waters. This study used artificial neural network (ANN) and convolutional neural network (CNN) algorithms to successfully create two classification models for fish species from Lake Toba: red devil fish (Amphilophus labiatus), mujahir fish (Oreochromis mossambicus), sepat fish (Trichogaster trichopterus). The purpose of this model is to automatically identify fish species by using image-based classification techniques. According to the study's findings, both models performed exceptionally well and had a high degree of accuracy. This study addresses the lack of effective automated fish classification systems for ecosystems like Lake Toba, Indonesia, which are threatened by invasive species such as the red devil fish. By comparing CNN and ANN models with different activation functions and optimizers, we found that CNN with rectified linear unit (ReLU) activation and Adam optimizer provides the most accurate and stable results. The findings offer practical implications for fisheries management and biodiversity conservation.
Experimental comparison of air, oil, and liquid nitrogen cooling media on the efficiency of a single-phase transformer Nugraha, Heri; Imaduddin, Agung; Priandana, Eka Rakhman; Hermawan, Asep Dadan; Darsono, Nono; Pramono, Andika Widya; Syahid, Adi Noer; Palaloi, Sudirman; Herbirowo, Satrio; Hendrik, Hendrik
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp25-35

Abstract

Transformers are critical component in electric power system, where minimizing energy losses is essential for efficiency and reliability. While ideal transformers operate with zero losses, practical transformers dissipate energy through winding and core losses caused by resistive heating. This study investigates the impact of three cooling media with ambient air, mineral oil, and liquid nitrogen on the efficiency and thermal performance of a 1 kVA single phase copper wound transformer. The experiment applied a resistive load under each cooling condition, recording input and output parameters using a HIOKI power meter model PW3360. Thermal behavior was monitored using infrared thermography and thermocouples. Copper winding resistivity was evaluated using a four-point probe within a cryogenic magnet system. The results show that liquid nitrogen cooling significantly reduced copper resistivity due to low-temperature conditions, achieving a transformer efficiency of 89.9%. Oil cooling improved efficiency to 86.0%, compared to 80.7% with air cooling. Although liquid nitrogen provided the greatest efficiency enhancement, its practical use is limited due to handling complexity and cost. In contrast, oil cooling offers a more feasible and effective solution for improving transformer performance in real world applications. These finding provide valuable insight for optimizing transformer thermal management strategies in power systems.
FinFET technology: a comprehensive review on materials, structures, fabrication, and device performance Rahman, Yead; Al Islam, Md Faiaz; Islam, Nafiya; Hassan, Sunzid; Shuvo, Sabbir Alom; Nabi, Iftesam; Alam, Jarif Ul
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp89-101

Abstract

As semiconductor devices become smaller, FinFETs have replaced traditional planar MOSFETs. Planar devices face issues like weak electrostatic control and high leakage current at small sizes. FinFETs solve these problems with a three-dimensional structure and multigate design. This improves gate control and reduces short-channel effects. This paper explains FinFET design, materials, and fabrication methods. It highlights how fin geometry affects current flow and device performance. Gate-source voltage (VGS) and drain-source voltage (VDS) are important parameters. These control the device operation in the lin-ear, saturation, and pinch-off regions. Performance factors such as on/off current ratio (ION /IOFF), subthreshold swing (SS), and drain-induced barrier lowering (DIBL) show that FinFETs work well for low-power and high-speed uses. Achieving uniform doping below 5 nm remains difficult. Atomic layer deposition (ALD) helps improve doping control. In summary, FinFETs play a key role in modern semiconductor design by improving scalability and efficiency.
Microbubble size and rise velocity measurement in dissolved air flotation system Alegría, Jeimmy Adriana Muñoz; Lopera, Jesús Emilio Pinto; Muñoz-España, Elena; Flórez-Marulanda, Juan Fernando
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp174-186

Abstract

Water reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved air flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens un-distortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research.
Optimizing hourly air quality index forecasting: a particle swarm optimization-enhanced hybrid approach combining convolutional and recurrent neural networks Khan, Darakhshan; Patankar, Archana B.; Kakar, Jyotika
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp333-341

Abstract

Air pollution is still a serious worldwide issue, and accurate air quality index (AQI) prediction is needed. This paper proposes a hybrid deep learning model integrating 1D convolutional neural networks (Conv1D) and long short-term memory (LSTM) networks, optimized with particle swarm optimization (PSO) to enhance AQI forecasting. The model was evaluated at six urban areas: Bandra, Thane, Mazgaon, Kurla, Nerul, and Malad, and compared with a single LSTM network. PSO adjusted hyperparameters like hidden units, batch size, epochs, and learning rate was used to improve predictive accuracy. The Conv1D+LSTM hybrid model drastically decreased RMSE by 49.19% (Bandra), 33.97% (Thane), 5.24% (Mazgaon), 20.52% (Kurla), 35.85% (Nerul), and 27.54% (Malad), and R² Score improvements up to 751.2%. Training logs indicated smoother convergence with loss decrease at faster rates compared to LSTM, showing better learning efficiency and generalization. By combining spatial and temporal feature extraction with automated hyperparameter tuning, this model captures sophisticated pollution patterns which increases the reliability of AQI prediction. Enhancements in the future can be adding regularization methods and more feature inputs to improve the accuracy.
Facial emotion recognition under face mask occlusion using vision transformers Maghari, Ashraf Yunis; Telbani, Ameer M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp395-403

Abstract

Facial emotion recognition (FER) systems face significant challenges when individuals wear face masks, as critical facial regions are occluded. This paper addresses this limitation by employing vision transformers (ViT), which offer a promising alternative with reduced computational complexity compared to traditional deep learning methods. We propose a ViT-based FER framework that fine-tunes a pre-trained ViT architecture to enhance emotion recognition under mask-induced occlusion. The model is fine-tuned and evaluated on the AffectNet dataset, which originally represents eight emotion categories. These categories are restructured into five broader classes to mitigate the impact of occluded features. The model’s performance is assessed using standard metrics, including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed framework achieves an accuracy of 81%, outperforming several state-of-the-art approaches. These findings highlight the potential of vision transformers in recognizing emotions under masked conditions and support the development of more robust FER systems for real-world applications in healthcare, surveillance, and human–computer interaction. This work introduces a scalable and effective approach that integrates self-attention, synthetic mask augmentation, and emotion class restructuring to improve emotion recognition under facial occlusion.
IDPS: A machine learning framework for real-time intrusion detection and protection system for malicious internet activity Fabiha, Raisa; Reberio, Stein Joachim; Farazi, Zubayer; Nur, Fernaz Narin; Sultana, Shaheena; Islam, A. H. M. Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp437-449

Abstract

With the increasing frequency and complexity of cyber threats, there is a pressing need for effective real-time solutions to detect and prevent malicious activities. This study introduces a novel machine learning-based architecture for real-time cybersecurity to enhance accurate identification and prevention of malicious cyber activities. The proposed framework combines advanced machine learning algorithms with Wireshark network traffic analysis to effectively detect and classify a wide range of cyberattacks, providing timely and actionable insights to cybersecurity professionals. A core component of this system is a prototype blocker, which is seamlessly integrated with Cisco infrastructure, enabling proactive intervention by blocking suspicious IP addresses in real-time. In addition, a user-friendly web application enhances system operability by offering intuitive data visualization and analytical tools, enabling rapid and informed decision-making. This comprehensive approach not only strengthens network security and protects digital assets but also equips defenders with the capability to respond effectively to the dynamic landscape of cyber threats.
Application of the model reference adaptive system method in sensorless control for elevator drive systems using 3-Phase permanent magnet synchronous motors Khoi, Tran Van; Anh, An Thi Hoai Thu; Hieu, Tran Trong
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp149-157

Abstract

Improving sensorless control performance in elevator drive systems using three-phase permanent magnet synchronous motors (PMSM) has become increasingly popular to reduce costs and enhance system stability. The primary operation of the elevator involves motor mode when the cabin moves upward and shifts to generator mode or braking mode under the influence of gravity when moving downward. This presents significant challenges for sensorless control. To address these issues, the model reference adaptive system (MRAS) based on the mathematical d-q axis model of the PMSM is proposed to estimate rotor speed and position. Combined with field-oriented control (FOC), this method optimizes performance and precisely controls motor torque without requiring physical sensors. Additionally, a low-pass filter is employed to process input signals, such as voltage and current, to improve estimation accuracy and optimize speed response. Simulation results from MATLAB/Simulink demonstrate highly accurate speed responses, particularly under continuous load variations.
Autonomous mobile robot implementation for final assembly material delivery system Firdaus, Ahmad Riyad; Sholihuddin, Imam; Hutasoit, Fania Putri; Naba, Agus; Suciningtyas, Ika Karlina Laila Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp158-173

Abstract

This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.

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