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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 48 Documents
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Search results for , issue "Vol 16, No 3: June 2026" : 48 Documents clear
An enhancement of stock price forecasting based on hybrid BiLSTM-Transformer model Vuong, Pham Hoang; Phu, Lam Hung; Duy, Le Nhat; Bao, Pham The; Trinh, Tan Dat
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1298-1306

Abstract

Stock price forecasting presents a challenging problem due to factors like nonlinearity, seasonality, and economic volatility in financial data. Deep learning approaches can handle nonlinearity and complexity of financial data, but they often face limitations in capturing both local and global dependencies. This study introduces a hybrid Transformer–bidirectional long short-term memory (BiLSTM) model to improve stock price forecasting. Our method combines the strength of BiLSTM with the global context understanding of the Transformer by embedding a 1D convolutional layer. The model can efficiently capture short-term and long-term dependencies in stock data. Experimental results on various datasets show that our hybrid model outperforms other well-known models.
A new multiplier less memcapacitor emulator with non-linear applications Basavanna, Suresha; Shankar, Chandra; S. B., Rudraswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1132-1147

Abstract

This study describes a memcapacitor emulator without a multiplier that make use of second-generation current conveyor (CCII), operational trans-conductance amplifier (OTA) and the fewest possible passive components. The proposed memcapacitor is proved mathematically and verified using several simulation approaches, such as process corner, non-volatile and hysteresis analysis. Also, provided the layout of CCII and OTA as well. The standard CMOS 90 nm technology is used in the Cadence Virtuoso tool to simulate the proposed memcapacitor emulator. This article also includes the use of memcapacitor emulator in the applications of R-C frequency selective network as well as adaptable neuromorphic structure. To investigate the experimental outcomes, an experimental setup was constructed with commercially available integrated circuits (ICs) CCII’s AD844AN and OTA’s CA3080EZ.
Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification Prenata, Giovanni Dimas; Ridho’i, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1562-1575

Abstract

Emotion classification based on electrocardiogram (ECG) signals has attracted increasing attention in affective computing and biomedical signal processing. However, training deep feedforward neural networks (DFFNN) using conventional gradient-based learning often suffers from local minima and slow convergence, particularly when dealing with nonlinear and limited datasets. This study presents a comprehensive performance analysis of single-stage and multi-stage metaheuristic optimization strategies applied to DFFNN for ECG-based emotion lassification in elderly participants. Five models were evaluated: Pure DFFNN, DFFNN optimized using genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and a hybrid multi-stage DFFNN+GA+GWO model. Experimental results from six independent trials demonstrate a substantial reduction in mean squared error (MSE) when metaheuristic optimization is applied. Pure DFFNN produced final MSE values in the range of 0.07462–0.08977, whereas DFFNN+GWO reduced MSE to 0.01894–0.02411. The proposed multi-stage DFFNN+GA+GWO achieved the lowest MSE of 0.014286 in the best run and an average MSE of approximately 0.0212 across trials. Training accuracy improved from 57.14%–66.67% (Pure DFFNN) to 80.95%–85.71% using metaheuristic pproaches. Although testing accuracy remained relatively stable at 33.33%–50.00% due to dataset size constraints, convergence behavior analysis shows that multi-stage optimization enhances stability and reduces oscillatory updates. These findings confirm that multi-stage metaheuristic optimization significantly improves training stability and error minimization in DFFNN models, offering a promising strategy for robust ECG-based emotion classification under small-sample conditions.
Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study Qureshi, Moiz; Ismail, Muhammad; Ahmad, Nawaz; Hussain, Ibrar; Ghoto, Abbas Ali; Vveinhardt, Jolita
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1339-1349

Abstract

This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Integrating Sustainable Development Goals into educational information systems: toward a theoretical model for sustainable school management Arinal, Veri; Miswanto, Miswanto; Setiawan, Kiki; Rahayu, Agus Tanti
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1350-1359

Abstract

This research addresses the critical challenge of implementing Sustainable Development Goal (SDG) 4, "Quality education," in Indonesian secondary schools. While national policies exist, schools lack a systematic digital tool to plan, monitor, and evaluate sustainability-based activities against concrete SDG indicators. To bridge this gap, this study employs a six-cycle design science research (DSR) methodology to develop a theoretical model for a sustainable education information system. The model is designed to integrate SDG principles into school management, enabling systematic data handling, adaptive curriculum functions, and real-time monitoring. A web-based prototype was developed using a React.js frontend and Node.js backend and evaluated through a mixed-methods approach. Data from interviews with 15 administrators and surveys of 97 teachers (yielding a usability satisfaction score of 4.34/5) validated the model’s effectiveness in making educational administration more efficient, transparent, and quality-oriented. The resulting artifact serves as a foundational technical and managerial reference for schools, education offices, and policymakers to leverage information technology in fostering a sustainable, participatory learning culture aligned with the SDGs.
Engineering intelligence for a sustainable and resilient future: from foundations to real-world impact toward the SDGs Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1075-1084

Abstract

The June 2026 issue of this journal presents a comprehensive body of research advancing efficient engineering intelligence from foundational theory to real-world deployment, with strong alignment to the Sustainable Development Goals (SDGs). A significant cluster addresses SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) through innovations in microbial fuel cells, high-voltage insulation reliability, artificial intelligence (AI) based battery management systems, and energy-efficient LoRa/LoRaWAN frameworks. These works emphasize energy sustainability, system resilience, and infrastructure optimization. A second cluster focuses on advanced electronics, control, and communication systems, including memcapacitor design, hybrid model predictive control, reflectarray antennas, and embedded intelligence for autonomous systems, demonstrating efficiency-driven engineering across hardware and system levels. A dominant cluster highlights SDG 3 (Good Health and Well-being), with applications in medical imaging, sepsis detection, breast cancer classification, and mental health analysis, leveraging deep learning, transformers, and hybrid AI models. Finally, contributions aligned with SDG 4 (Quality Education) explore gamified learning systems, virtual reality adoption, and SDG-integrated educational information systems, while complementary studies in agriculture, finance, and Internet of Things (IoT) further demonstrate the societal impact of intelligent systems. Collectively, these works reinforce the role of efficient, scalable, and data-driven engineering in addressing global challenges.
Radar-based gesture recognition simulation for unmanned aerial vehicles command interpretation Dermawan, Denny; Kurniawan, Freddy; Astuti, Yenni; Setiawan, Paulus; Lasmadi, Lasmadi; Mauidzoh, Uyuunul; Sudibya, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1227-1235

Abstract

Radar-based gesture recognition has emerged as a robust alternative to vision-based systems, particularly in environments where lighting and privacy pose challenges. This study presents a simulation approach for recognizing hand gestures to control unmanned aerial vehicles (UAVs) using radar signals. Five discrete gestures, i.e., TakeOff, Land, MoveForward, TurnLeft, and stop, were defined and modeled in MATLAB to generate synthetic radar signals. From each sample, four time-frequency domain features were extracted: duration, maximum amplitude, dominant frequency, and root mean square (RMS). A dataset of 500 samples (100 per class) was classified using three supervised learning models: support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree. The k-NN classifier achieved the highest accuracy of 96%, demonstrating the feasibility of lightweight classifiers for gesture recognition using low-complexity features. These results highlight the potential of radar-based interfaces to replace traditional remote controls in UAV operation. The proposed simulation framework contributes to the development of intuitive, non-contact human-machine interaction systems.
Utilizing phase congruency technique in reception performance optimization of UWB signals in multipath fading channels Abdelaziz, Nadir Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1272-1285

Abstract

Ultra-wideband (UWB) technology enables high-data-rate communications and centimeter-accurate indoor localization but suffers severe degradation in multipath fading channels due to dense multipath components, narrowband interference (NBI), and low signal-to-noise ratios (SNR). Conventional energy-based detection methods, including Rake receivers, fail under these conditions due to amplitude sensitivity. This paper introduces a phase congruency (PC)-based selective Rake (S-Rake) receiver that exploits phase alignment across frequencies rather than signal magnitude for robust feature detection. The proposed method computes PC metrics via Hilbert transforms and sub-band decomposition to identify phase-aligned multipath components, guiding S-Rake finger selection (4, 8, and 128 fingers) and time-of-arrival (TOA) estimation. Simulations using 6th-derivative Gaussian pulses over IEEE 802.15.3a CM4 channels (NLOS, 4-10 m) with AWGN and IEEE 802.11a interference (SIR=-30 dB to 0 dB) demonstrate that PC-based S-Rake achieves 4 dB SNR gain at BER=10⁻⁴ over conventional Rake under high interference. DS-UWB with PC outperforms TH-UWB by 3× lower BER at SIR=-30 dB. Increasing Rake fingers from 4 to 128 reduces BER by >40% and improves TOA accuracy by 62% (RMSE: 1.8 ns → 0.68 ns). PC maintains BER=10⁻³ at SIR=0 dB where conventional methods fail. Results establish PC as a transformative paradigm for interference-resilient UWB applications including IoT localization and 5G-coexistent communications.
Residual reinforcement learning for disturbance-resilient control under modeling uncertainties Adetifa, Abolanle; Donatus, Rexcharles Enyinna; Udekwe, Daniel
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1175-1187

Abstract

Modern control systems must operate reliably in the presence of modeling uncertainties and external disturbances, conditions under which conventional fixed-gain controllers often exhibit performance degradation. This paper proposes a residual reinforcement learning framework for disturbance-resilient pitch-rate control of an aircraft longitudinal model. A classical proportional-integral-derivative (PID) controller is employed as a stabilizing baseline, while a deep deterministic policy gradient (DDPG) agent learns a bounded residual control signal to compensate for unmodeled dynamics and external perturbations. To promote favorable transient behavior, the learning process incorporates transient-aware and reference-model-based reward shaping, while actuator constraints are enforced within the environment dynamics. Simulation results demonstrate that the proposed residual controller achieves a superior balance between response speed, overshoot, and tracking accuracy compared with both the standalone PID controller and a pure DDPG-based controller. In particular, the residual architecture significantly reduces overshoot and tracking error while preserving fast transient response and providing robust disturbance rejection under large pitching moment disturbances. These results indicate that residual reinforcement learning offers a practical and effective approach for enhancing robustness and performance in safety-critical flight control applications.
Bioelectricity generation and physicochemical evolution of a substrate with sheep compost in microbial fuel cells in a high Andean area Colonio, Joel; Carmen, Elvis; Lozano, Arlitt; Colonio, Alizze
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1085-1096

Abstract

The recovery of organic waste, such as sheep compost, is a key strategy for energy valorization. This study evaluated its potential as a substrate in microbial fuel cells (MFCs) using zinc (anode) and copper (cathode) electrodes and analyzed the evolution of its physicochemical properties, using soil samples from a high Andean area of the Chacapampa district, Peru. Two configurations of ground-mounted MFCs in series were compared: C1 (16 reactors of 400 g) and C2 (8 reactors of 800 g), maintaining a total mass of 6.4 kg. The C2 configuration was significantly more efficient, generating a median power of 819.53 μW, more than double the 380.92 μW of C1 (p=0.002). The final physicochemical analysis revealed that the process transforms the substrate, increasing electrical conductivity and phosphorus availability, although potassium decreased. It is important to note that due to the use of reactive metal electrodes, the system operates as a hybrid microbial-galvanic cell, where the zinc anode is consumed. It is concluded that sheep compost is an effective substrate and that consolidating the volume in fewer reactors optimizes electrochemical performance, although long-term environmental impacts regarding zinc accumulation must be monitored.

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