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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 6,439 Documents
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The adoption of artificial intelligence in government technology in developing countries: a systematic literature review Maria Florentina Rumba; Flourensia Sapty Rahayu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2192-2209

Abstract

This study systematically reviews empirical evidence on Artificial Intelligence adoption in Government Technology across developing countries, addressing three research questions regarding determinant factors for success, strategies to overcome absorptive capacity constraints, and governance frameworks for responsible implementation. To answer these questions, a systematic literature review was carried out using a structured methodology: The Scopus database was searched with a comprehensive keyword strategy, which initially yielded ninety-three publications. Through rigorous screening and eligibility assessment, fourteen articles meeting all inclusion criteria were analyzed thematically. The findings demonstrate that successful AI adoption requires holistic alignment among institutional factors including policy frameworks and public trust, organizational elements such as human resource capacity and bureaucratic culture, and environmental conditions encompassing infrastructure and societal demands. Effective strategies identified include strengthening digital infrastructure, developing human capital, implementing adaptive governance, fostering multi-stakeholder collaboration, and ensuring contextual adaptation. The study further reveals that legitimate and sustainable implementation necessitates integrating collaborative governance as a legitimacy foundation, Governance, Risk, and Compliance (GRC) frameworks as ethical control systems, and Explainable AI as a transparency mechanism. This integrated approach enables AI to generate both operational efficiency and strategic public value, including social inclusion and progress toward sustainable development objectives.
Indirect adaptive neural network control for constant power conversion in wave energy system Jesus de la Cruz-Alejo; Hugo Beatriz Cuellar; J. Antonio Lobato Cadena; Edwin Christian Becerra-Alvarez
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2120-2133

Abstract

The conversion of ocean wave energy into electrical energy occurs near beaches and is important for the design and implementation of wave energy conversion (WEC) systems. However, its generation depends on environmental conditions, which complicates the design and control of the devices. This work presents an approach to indirect adaptive control based on artificial neural networks to detect wave conditions for the proper functioning of WEC structures. The method involves generating a constant output voltage using a voltage boost converter and a direct current-alternating current (DC-AC) converter. Maintaining a constant output power despite variations in wave conditions to generate a voltage of 24 V with a current of 2 A is the primary proposal for the control design. The mechanical design integrates a rack and pinion system and a pulley transmission that connects a floating device to an electric generator. The implementation of control is carried out on an Arduino platform. The control system was implemented on an Arduino platform, occupying 48% of the available memory, with a convergence time of 4.29 ms, a mean squared error (MSE) of 0.13715, and a root mean squared error (RMSE) of 0.37034. These low values indicate that the proposed control system has greater accuracy. The experimental results validate the proposed control system, which reduces energy conversion errors and achieves greater efficiency.
Lightweight face recognition based on PCA coupled with PSO-based feature optimization in resource-constrained environments Chaimaa Khoudda; Zineb Gotti; Salma Azzouzi; Moulay El Hassan Charaf
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2134-2157

Abstract

We present a streamlined PCA-PSO structure of lightweight face recognition in this paper, which combines principal component analysis (PCA) to reduce dimensions with particle swarm optimization (PSO) to adaptively select features. In contrast to traditional PCA-based models, our model dynamically chooses the most informative subset of the 44 features and results in a small but informative representation. The approach has a high recognition accuracy of 99.99 on ORL and 98.45 on LFW, with low latency (approximately 3 seconds per epoch) and low memory footprint, and without the need to use a graphic card to run it. Extensive computational studies have shown that the proposed pipeline is much faster and consumes less memory than PCA-ACO and lightweight convolutional neural network (CNN) models like MobileNetV2 and Squeeze Net and is best suited to run in real-time in CPU-limited environments. The statistical tests prove the betterment of our method compared to the past methods, where the changes are statistically significant. This research offers an efficient, simple, yet scalable alternative to deep learning-based recognition systems, especially when embedded integration is a key requirement, by offering rapid convergence, adaptive feature selection, and compact representation.
The life-cost cycle-based sizing of complementary energy storage technologies in DC microgrids Dunya Sh. Wais; Huda A. Abbood; Radhi Sehen Issa
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1724-1734

Abstract

Hybrid energy storage system (HESS) configurations have the potential to mitigate the detrimental effects of photovoltaic power generation oscillations on DC microgrid safety and reliability. The economic efficiency of HESS can be improved through the utilization of batteries and their complementary attributes through an energy management strategy. This will allow for the full utilization of the benefits of superconducting magnetic energy storage (SMES), such as high efficiency, lossless energy storage, elevated power density, and rapid response. The battery-SMES HESS is subject to a life cycle cost (LCC) model, along with its associated constraints. System expenditures can be drastically cut by optimizing the HESS capacity layout. The goal function is the lowest LCC, presuming that the power demands of the system are met. Particle swarm optimization takes acceleration into account when designing the capacity of the system. In order to prove that the suggested method of configuring capacity works, a microgrid model is created and tested using numerical data.
Semantic-aligned multimodal human activity recognition using visual and audio data Yeeun Park; Junhoo Byun; Siwoo Byun
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2087-2095

Abstract

Human activity recognition (HAR) requires robust performance under heterogeneous sensing conditions for practical deployment. However, single-modality approaches are limited in capturing the rich contextual information inherent in complex human behaviors. This paper presents a semantic-aligned multimodal HAR framework that integrates visual and audio information without assuming instance-level synchronization. To address dataset heterogeneity, samples from the HMDB51 video dataset and the ESC-50 audio dataset are aligned by mapping fine-grained classes into a shared high-level activity label space. For each modality, ResNet-18-based models are trained independently using frame-based visual inputs and 64-bin Mel-spectrogram-based audio representations. During inference, the output logits of the two models are combined through score-level weighted linear fusion. Experimental results show that the proposed multimodal approach consistently outperforms unimodal baselines in terms of accuracy and Macro-F1 score, with particularly notable improvements in activity groups where environmental context plays a significant role. These findings indicate that semantic-aligned score-level fusion can improve recognition robustness even under mismatched dataset conditions.
Bone strength analyzer and monitoring device for lower limb external fixation Devin Babu; Waheb A. Jabbar; Muhammad Hisyam Rosle; Noorazliza Sulaiman; Mohd Amir Shahlan Mohd Aspar; Abdul Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1778-1791

Abstract

The procedure for external fixator removal in lower limb fractures is typically based on radiographic data, subjects’ patients to ionising radiation, and provides minimal real-time information about the healing process. This project suggests a sensor-based Internet-of-Things apparatus, which will measure bone strength during the recuperating process based on load cells, HX711 amplifiers, and a Wemos ESP8266 microcontroller. The system provides real-time feedback in the form of LED indicators and a buzzer, whereas remote monitoring is supported by the Blynk dashboard. Measurement accuracy of over 90% was carried out as per the experimental validation conducted under the simulation of various loads (3-9 kg) and with clear stage indicators of critical, partial, and full recovery. The device offers continuous monitoring and objective bone healing evaluation with no radiation in comparison to conventional imaging. The limitation of the study is the limited range of loads in prototype testing, which could affect accuracy in particular situations. However, the results represent the possible clinical relevance of introducing real-time biomechanical surveillance into the process of fracture treatment, hence contributing to safer rehabilitation and more reasonable decisions related to the fixator removal.
A new diagnostic method based on support vector machine for short circuit winding faults in induction motors Hicham Zaimen; Tawfik Thelaidjia; Makhlouf Chouki; Abdurrahman Ünsal
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1735-1754

Abstract

Inter-turn short circuits (ITSCs) in induction motor (IM) windings are among the most critical and frequent faults in industrial environments, as they can rapidly evolve into severe damage, leading to unplanned downtime and costly maintenance. To enhance the reliability of IMs, this paper proposes a machine learning–based diagnosis method dedicated to ITSC failures. The developed diagnostic tool combines a support vector machine (SVM) classifier with Fisher’s ratio (FR)-based feature selection. The proposed framework uses experimentally acquired current signals under healthy conditions and five ITSC fault severity levels (1%–5%), evaluated across four load conditions (25%, 50%, 75%, and 100%). Each signal is segmented into 200 non-overlapping segments (500 samples each), from which nine time-domain features are extracted to capture fault-related characteristics. These features are then used for training and testing a SVM classifier capable of distinguishing between healthy states and levels of severity of ITSC faults. To optimize the classification process, the Fisher’s ratio (FR) algorithm is employed to select the most informative features while discarding those with low relevance. Our findings unveiled that the proposed hybrid FR-SVM-based diagnosis achieves high diagnostic accuracy ranging from 99.54% to 100%. Furthermore, the outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
Teaching analysis of sub-synchronous resonance of thermal power plants using virtual laboratory Sugiarto Kadiman; Ratna Kartikasari
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1677-1687

Abstract

The development of a MATLAB/Simulink-based virtual laboratory for studying sub-synchronous resonance (SSR) offers a robust educational platform for analyzing complex power system interactions. Based on the IEEE first benchmark model, this virtual environment provides a safe, efficient, and comprehensive tool for engineering students to study the dangerous interactions between series-compensated lines and turbo-generator shaft systems. The simulation features a 920 MVA, 60 Hz turbo-generator connected to an infinite bus through a series-compensated line. Students can analyze torsional interaction, which result from energy exchange between the electrical network and the mechanical shaft. Higher degrees of series compensation increase the risk of SSR, as the electrical resonant frequency matches the complement of one of the mechanical shaft torsional modes.
Performance analysis of 5G NR network planning at 2300 MHz in Indonesia: A comparative study of LoS and NLoS scenarios Putri Rahmawati; Lia Hafiza; Muhammad Adam Nugraha; Syifa Maliah Rachmawati
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1944-1954

Abstract

This study presents a systematic comparative analysis of 5G new radio (NR) network planning at 2300 MHz for Bandung City, Indonesia, evaluating line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios to determine infrastructure requirements and performance characteristics for urban deployment. Employing the 3GPP TR 38.901 Urban Macro propagation model, link-budget analysis, and Atoll-based simulation for a 167.31 km² urban area, the study evaluates coverage performance under projected 2025 deployment conditions. The results reveal significant differences between propagation scenarios. LoS conditions require 45 gNodeBs for uplink coverage, achieving a synchronization signal reference signal received power (SS-RSRP) of -94.63 dBm and a synchronization signal signal-to-interference-plus-noise ratio (SS-SINR) of 10.87 dB, both categorized as “Good.” In contrast, the NLoS scenario requires substantially denser deployment with 635 gNodeBs, resulting in improved SS-RSRP performance of -71.98 dBm (“Excellent”) and SS-SINR of 12.32 dB (“Good”), along with more uniform coverage distribution. The findings indicate that improved KPI performance and coverage uniformity in NLoS environments can be achieved through substantially increased infrastructure density, highlighting the trade-off between network quality, deployment complexity, and infrastructure cost in urban 5G NR planning.
Design and characterization of a flexible ultra-low-power analog front-end circuit using organic thin-film transistors for wearable electrocardiogram monitoring Suhad Qasim G. Haddad; Ali Falih Chlloob
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1767-1777

Abstract

The shift from reactive to proactive healthcare has accelerated the development of flexible, comfortable, and energy-efficient wearable health monitoring systems. This study addresses a critical technological gap: the inherent trade-off between the mechanical flexibility of organic thin-film transistors (OTFTs) and their limited electrical performance (low charge carrier mobility) compared to rigid silicon-based technologies. To overcome this, we outline the system-level design of a fully configurable analog front-end (AFE) utilizing OTFTs for precise electrocardiogram (ECG) measurement. A theoretical transfer-function-based co-design approach is used to balance gain, noise rejection, and power efficiency via high-fidelity MATLAB/Simulink continuous-time simulations. Simulation results demonstrate that the proposed AFE achieves a 40 dB gain, a precise diagnostic bandwidth of 0.5–150 Hz, a common-mode rejection ratio (CMRR) of 65 dB, and an ultra-low power consumption of 33 µW. These metrics strictly align with standard clinical ECG requirements, outperforming current state-of-the-art all-organic architectures primarily in power efficiency. Consequently, the input signal-to-noise ratio (SNR) is significantly enhanced by 24.53 dB (from 8.01 to 32.54 dB). The novelty of this work lies in achieving silicon-like clinical functionality within an all-organic structure at minimal power. This establishes a new benchmark for flexible electronics and paves the way for invisible, skin-like devices for continuous cardiovascular monitoring.

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