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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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Comparative performance analysis of lightweight face identification algorithm Wuyun Wang; Suchada Sitjongsataporn
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.pp2042-2060

Abstract

With the wide application of face recognition in resource-constrained scenarios like mobile and embedded devices, lightweight algorithms have become a research focus, but existing studies lack multi-dimensional, scenario-based performance comparisons. This paper studies the performance evaluation and application adaptation of lightweight face recognition algorithms, innovatively builds a scenario-based evaluation system, verifies the performance improvement of combining traditional algorithms with MobileNet, and constructs an efficient, stable and low-cost system. It elaborates on face recognition principles, including key links of face detection, feature extraction and matching, introduces traditional algorithms such as Eigenfaces, Fisherfaces and LBPH, and focuses on MobileNet’s characteristics: reducing computation and parameters via depthwise separable convolution, and adjustable width and resolution. Four comparative experiments verify the "traditional algorithms + MobileNet" hybrid strategy. Results show the combination achieves 98.1% accuracy, 4.3 percentage points higher than single MobileNet; LBPH + MobileNet balances performance and resource consumption best, with 110MB memory, 40% CPU usage and 315ms processing time. The hybrid strategy improves accuracy and efficiency in different scenarios, aiming to provide a scientific basis for the engineering application and subsequent optimization of lightweight face recognition algorithms, and supporting algorithm selection and performance improvement in resource-constrained scenarios.
ACLiMA: an IoT-based autonomous flood monitoring and mitigation system with database-driven threshold control Hendi Santoso; Rizqan Khairan Munandar; Apriansyah Apriansyah; Andi Ihwan; Putri Yuli Utami
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.pp1867-1875

Abstract

Urban flooding remains a critical challenge in densely populated and low-lying areas, where delayed response and limited monitoring infrastructure significantly increase flood risks. Existing flood monitoring systems are typically limited to passive observation or fixed-threshold alerting without integrated autonomous mitigation and flexible configuration. This study proposes autonomous control logic for IoT-based monitoring and actuation (ACLiMA), an IoT-based autonomous flood monitoring and mitigation system using a database-driven threshold control approach to enable real-time monitoring and immediate response. The system integrates ultrasonic water-level sensing, centralized database management, web-based visualization, and autonomous pump actuation within a unified architecture. Flood conditions are classified into four operational states—SAFE, CAUTION, DANGEROUS, and FLOOD—based on configurable threshold values stored in the database, allowing dynamic adjustment without firmware modification. Experimental results demonstrate stable system integration with deterministic control behaviour and low response latency between sensing and actuation, enabling timely pump activation during critical conditions. The system also provides multi-temporal visualization for monitoring and analysis, while the database-driven configuration enhances flexibility, scalability, and ease of deployment across different environments. Overall, the proposed system offers a low-cost, modular, and autonomous solution for real-time flood mitigation, contributing to the transition from passive monitoring toward active mitigation in smart city and resource-constrained urban applications.
Edge-aware coffee aroma classification using multi-representation feature extraction and LightGBM on Jetson Nano Denda Dewatama; Erni Yudaningtyas; Muhammad Fauzan Edy Purnomo; Setyawan Purnomo Sakti
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.pp2096-2105

Abstract

Objective coffee aroma evaluation remains challenging outside controlled laboratory settings, and most electronic nose studies neglect embedded deployment constraints. This work proposes an edge-aware coffee aroma classification framework that integrates multi-representation feature extraction with LightGBM and evaluates both predictive performance and computational efficiency. A six-sensor metal-oxide semiconductor (MOS) e-nose was developed, producing a balanced dataset of 1,080 trials from 12 aroma classes. Five feature representations were investigated, including baseline signals, autoencoder embeddings, and convolutional features derived from pseudo-image transformation. Experiments on an NVIDIA Jetson Nano using stratified five-fold cross-validation showed that residual-based representations significantly improved performance. The lightweight residual network achieved an accuracy of 0.9972 with low training time and memory usage. Pareto analysis confirms that optimal performance is achieved by balancing accuracy and resource constraints, thereby enabling reliable deployment in edge and IoT environments.
Design and manufacture of a self-balancing system for two-wheeled vehicle models using a reaction wheel Indrawanto Indrawanto; Yuzar Arigi; Vani Virdyawan
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.pp1853-1866

Abstract

Motorbikes are a popular mode of transportation in Indonesia and are agile in maneuvering on roads with heavy traffic. The increasing use of motorbikes has triggered many accidents. This paper discusses the design, manufacture, and control of a self-balancing system for a two-wheeled vehicle model to improve driving safety. The self-balancing system designed uses a reaction wheel. The system architecture consists of a microcontroller board, a DC motor, a gyroscope, a reaction wheel, and a two-wheel vehicle model. The dimensions of the reaction wheel are optimized between the mass and the moment of inertia to make it possible to self-balance the model from a certain initial angle. The controller is designed based on the state space model with a feedback linear-quadratic regulator controller. The matrix weighting values are selected using Bryson’s rules method. Experimental results show that the self-balancing system can work well for the two-wheel vehicle model.
Miniaturized patch antenna for the S-band communication subsystem of the 3U University CubeSat Nabil El Hassainate; Loubna Berrich; Nabil Benjelloun; Ahmed Oulad Said; Zouhair Guennoun
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.pp1913-1926

Abstract

This paper introduces a miniaturized patch antenna for the reception module of the 3U University CubeSat in the S-band communications subsystem. In order to reduce the physical characteristics of the antenna (dimensions, mass) and achieve circular polarization (CP), as well as increasing its performances, two techniques are used: the first consists of introducing semicircle truncation on both sides of the square patch, and the second consists of modifying the ground plane with networks of symmetrical slots along the main axes (x,y). The fabricated antenna prototype has overall dimensions of 55×55×3.27 mm and a total mass of 20.59 g. The developed antenna spans the uplink band (2.025 to 2.110 GHz) for payload and telemetry operations. The designed antenna achieves a reflection coefficient below minus 10 dB across the target frequency band, along with a minus 3 dB axial ratio bandwidth that is well appropriate to space communication links. The comparisons of the prototype results to the simulation results using CST and HFSS provide close agreement of around 90%.
Integrating principal component analysis in spatial-spectral fusion models for hyperspectral image segmentation Alexander Calvin; Laksmita Rahadianti
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.pp2074-2086

Abstract

Hyperspectral imaging (HSI) from unmanned aerial vehicles (UAVs) provides rich spatial-spectral data, but its high dimensionality presents significant computational challenges for semantic segmentation. While state-of-the-art models like the transformer-based HSI-TransUnet are often employed, they introduce massive computational overhead. This study adapts a lightweight, dual-tunnel deep convolutional neural network (DCNN) framework for land-use segmentation on hyperspectral images by integrating PCA-based spatial reduction in the spatial branch, and benchmarks it on the UAV-HSI-Crop dataset against HSI- TransUnet. For further analysis, an ablation study compares principal component analysis (PCA) and local similarity projection (LSP) as spatial feature ex- tractors. The results demonstrate a significant performance and efficiency advantage. Our proposed PCA-based model (271.1K parameters) obtained a Kappa (κ) of 0.8582, overall accuracy (OA) of 0.8800, and average accuracy (AA) of 0.4918, outperforming the LSP-based model by 0.65% in κ, 0.51% in OA, and 2.16% in AA and the HSI-TransUnet baseline by 2.35% in κ, 1.95% in OA, and 8.10% in AA. On our experimental setup, this result was achieved with a 152.7-fold reduction in model size, a 14.2-fold decrease in training time, and a 4.6-fold speedup in inference relative to the reported HSI-TransUnet baseline. These findings show that the PCA-based dual-tunnel DCNN provides a favor- able trade-off between class-balanced accuracy and computational efficiency for this HSI segmentation task.
Comparative analysis of multi criteria decision making method performance for hotel selection: a meta-decision framework using grey wolf optimization Nour El Houda Fethellah
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.pp1998-2013

Abstract

The selection of an optimal hotel is a classic multi criteria decision making (MCDM) problem involving multiple conflicting criteria. While numerous MCDM methods exist, their results often vary significantly, creating uncertainty for decision-makers. To address this challenge, this study introduces meta-decision framework that leverages the grey wolf optimizer (GWO) to compare thirteen MCDM methods for hotel selection. The framework employs GWO to establishing a common foundation for impartial evaluation. Each method is assessed through a comprehensive suite of metrics quantifying consensus alignment, stability, Pareto efficiency, and ranking quality. The robustness of each method was evaluated using Monte Carlo simulations, while results were aggregated via Borda Count, Jaccard similarity indices, and Pareto efficiency analysis. The results showed that individual methods frequently disagreed. However, a clear consensus emerged: hotel H415 ranked first across seven methods and achieved the highest overall score, confirming it as the best choice. Among the methods, TOPSIS proved the most stable under changing conditions, while SAW and WASPAS aligned most closely with other methods. The proposed ensemble approach mitigates single-method bias, offering a more reliable foundation for complex decision-making and valuable insights for enhancing decision reliability in MCDM applications.
Real-time facial and body pose emotion recognition for children with autism based on YOLOv8 and LSTM Siti Nurohmahwati; Ananda Putra Kanieza; Ade Rifky Setiawan; Ahmad Fadlan
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.pp1899-1912

Abstract

Children with autism spectrum disorder (ASD) often face challenges in recognizing and expressing emotions, which can affect their behavior and participation in inclusive classroom environments. This study proposes a real-time multimodal emotion recognition system integrating deep learning and Internet of Things (IoT) technologies to support early emotional monitoring in children with ASD. The framework combines YOLOv8 for facial expression detection and YOLOv8-based pose estimation for body movement analysis, along with a long short-term memory (LSTM) network for temporal emotion classification. At the facial level, the system recognizes five emotional states: sad, happy, neutral, boredom, and tantrum. At the temporal level, the LSTM model classifies behavioral sequences into three categories: neutral/bored, happy, and tantrum, enabling hierarchical emotion interpretation from instantaneous expressions to temporal patterns. Experimental results show that the facial expression model achieves 92% precision, while the LSTM-based classifier reaches 95% peak validation accuracy and 93.33% final test accuracy. The system is deployed on a web- based monitoring platform with real-time notifications for educators and parents. The proposed approach demonstrates effectiveness in providing timely emotional insights to support early intervention and improve inclusive education for children with ASD.
Aquaponic greenhouse agriculture integrated with multi-modal sensors and LED-grow-light IoT-based Pujianti Wahyuningsih; Muhammad Risal; Nining Haerani; Abdul Jalil
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.pp2254-2264

Abstract

This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.
A non-interactive lattice-based scheme supporting multiple blindness modes Dinh Hai Le; Luong Vu Ngoc Bui
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.pp1976-1984

Abstract

This paper proposes a lattice based multi-mode blind signature framework that uniformly supports three operating modes: full blindness, partial blindness, and non-blindness. The design employs public matrices with trapdoors, combined with hash functions mapping into ℤ??, short preimage sampling mechanisms, and an identity encryption component to ensure fairness and enable traceability in case of misuse. Based on this construction, the user generates a blinded request, the signer produces a short response bound to the encrypted identity, and the user performs an unblinding step to obtain the final signature. The paper also presents the system model, research methodology, security claims, and parameter discussions in the post-quantum setting. The main properties analyzed include correctness, blindness, partial blindness, existential unforgeability, one more unforgeability, fairness, and traceability under the SIS and one more SIS assumptions. The results indicate that the proposed approach provides a flexible solution for applications requiring a balance between anonymity, accountability, and post-quantum security.

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