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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,338 Documents
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Design of a cost-effective online experimental platform for electrical experiments using a Raspberry Pi-based system Abdelkrim Benali; Somia Benali; Benameur Hemidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp337-348

Abstract

Following the COVID-19 pandemic, online learning platforms have become vital for supporting distance education. This work presents LABTEC, an online Experimental Platform for electronics education that enables students to manipulate real hardware through a learning management system (LMS). The platform allows remote execution of experiments with electronic circuits and instruments, such as oscilloscopes, providing hands-on practice over the Internet in real time. The main contributions of this work are threefold: (i) a hybrid Flask–Django server architecture, where flask manages instrument-level control and Django provides secure and scalable web services; (ii) the use of a Raspberry Pi gateway as a cost-efficient and versatile hardware interface; and (iii) an open-source remote laboratory framework experimentally validated to support real-time interaction with average end-to-end latency below 50 ms, stable multi-user access, and low resource utilization. Experimental results demonstrate reliable operation under concurrent user scenarios, achieving consistent measurement visualization and control with reduced deployment cost compared to proprietary and institution-centric remote laboratory platforms. Performance evaluation shows a control latency below 50 ms for closed-loop tasks, a success rate above 98% under multi-user access, and average CPU and RAM usage of 35% and 420 MB on Raspberry Pi 4B during peak load. These results demonstrate that the system is responsive, reliable, and suitable for concurrent experiments. Although validated with a single instrument type, the proposed approach offers a scalable and replicable solution that can significantly enhance electronics education and lower laboratory infrastructure costs.
Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression Milind Paraye; Rajendra G. Sutar
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp786-797

Abstract

Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Margin-reciprocal loss: enhancing robust network anomaly detection on imbalanced traffic data Rachid Tahri; Abdellah Ouammou; Abdellatif Lasbahani
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp498-508

Abstract

Accurate detection of network intrusions remains challenging under severe class imbalance, where rare attacks such as remote-to-local (R2L) and user-to-root (U2R) are poorly represented. Although many learning-based intrusion detection systems achieve high overall accuracy, conventional loss functions often bias training toward majority classes, leading to weak minority-class performance. This paper introduces a smooth margin-reciprocal loss (MRL), inspired by distance-weighted discrimination (DWD), which emphasizes samples with small or negative margins while rapidly attenuating penalties for well-classified instances. Unlike probability-based focal loss, MRL operates directly on the signed margin and enables stable optimization with first-order methods. Experiments conducted on the NSL-KDD benchmark using linear and shallow multilayer perceptron models show that MRL consistently improves macro-F1 and per-class precision–recall AUC compared with hinge, logistic, and focal losses, with notable gains on minority attack classes.
Deep learning in cryptanalysis a comprehensive review of techniques, applications, challenges, and future trajectories Oussama Noui; Amine Barkat
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp846-855

Abstract

The integration of deep learning (DL) into cryptanalysis represents a paradigm shift, challenging traditional mathematical approaches and unlocking novel attack vectors against cryptographic algorithms and implementations. This comprehensive review synthesizes findings from recent pivotal publications to provide an in-depth analysis of the current state-of-the-art, methodologies, empirical successes, fundamental limitations, and future potential of DL in cryptanalysis. We focus extensively on two primary domains DL-based side-channel analysis (DL-SCA) and DL-enhanced cryptanalysis of symmetric primitives. The review meticulously examines advancements in attack efficiency (reducing the number of traces/queries), robustness against sophisticated countermeasures, automated feature extraction, and the nascent exploration of theoretical foundations. While DL demonstrates remarkable capabilities in automating complex pattern recognition critical to cryptanalysis, significant challenges persist, including the “black-box” nature of models, data dependency, scalability to full cryptographic primitives, and the critical need for explainability and theoretical grounding. This review serves as a foundational resource for researchers and practitioners navigating this rapidly evolving intersection of artificial intelligence and cryptography.
Optimization of glioma segmentation using 3D U-Net++ in MRI surgical planning and patient safety outcomes Ahmed Bounegta; Mustapha Khelifi; Mohammed Beladgham
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp798-808

Abstract

The main goal of this study is to develop and evaluate a novel 3D U-Net++ convolutional neural network for accurate segmentation of glioma sub regions in MRI scans, aiming to enhance surgical planning, targeted radiotherapy, and patient safety. Precise segmentation of glioma sub-regions is a persistent challenge in neuro-oncology due to substantial morphological variability across patients. To address this, we introduce an automatic segmentation model based on a 3D U-Net++ architecture with dense skip connections, which improves spatial feature extraction and the delineation of tumor boundaries. Utilizing volumetric data from the BraTS 2020 benchmark, the model automatically segments three clinically relevant substructures: tumor core, the enhancing tumor, and whole tumor. The integration of dense connections with 3D convolutional layers facilitates the detection of subtle tissue variations, including necrosis and edema. Quantitative evaluation demonstrates that the proposed 3D U-Net++ surpasses conventional architectures such as standard U-Net and DeepMedic in Dice coefficient, sensitivity, and specificity, yielding more homogeneous and continuous segmentations while reducing manual and semi-automatic annotation efforts. This approach supports advanced clinical decision making and workflow automation, and offers potential for application to other tumor types or integration into real-time clinical practice.
Fairness dynamics in graph neural networks: a comparative study of graph-structured neural models with and without gradient-based training Ananda Chatterjee; K A Venkatesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp403-413

Abstract

Graph neural networks (GNNs) are gaining more and more popularity in high stakes domain due to their ability to learn both from features and relationships. Nevertheless, there are concerns regarding how this accuracy centric optimization used by these models will impact fairness when deployed in socially sensitive areas. This work explores the interplay between predictive accuracy and fairness in GNNs when applied in judicial risk assessment system. A comparative study was performed among three canonical architectures such as graph convolutional networks (GCN), graph sample and aggregate (GraphSAGE) and graph attention networks (GAT) under trained and untrained settings on judicial risk assessment dataset. Fairness was evaluated through metrices like demographic parity (DP), equalized opportunity (Eopp), and equalized odds (Eodds) along with predictive performance metrices. Sensitivity analysis was conducted to investigate the effect of graph construction choices and neighborhood sizes in determing fairness and predictive accuracy. Experimental evidences proved that backpropagation improved predictive performance but in tandem fairness degradation happened. Untrained models exhibited lower fairness gap but that is superficial as weak predictive outcome of those models made group differences suppressed. Among the three trained models GAT was able to strike a good balance between accuracy and fairness while increase in neighborhood size caused little bit improvement in fairness via graph smoothing. The novelty of this work lies with its empericial characterization of GNNs under realistic settings. This study emphasizes the fact that how learning methodology, architectural designs, graph formation influence fairness outcomes. This work enlightens how graph-based models can be applied to decision making scenario and encourages embedding of fairness aware training strategies to it.
A lightweight architecture for IoT based on blockchain, designed for constrained IoT devices Yassin Elgountery; Mohamed Aghroud; Meryem Lasaad; Mohamed Oualla
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp596-608

Abstract

Blockchain is a technology that is evolving day by day, characterized by features such as security, decentralization, immutability, traceability, and privacy protection. These features make it a promising solution for internet of things (IoT) systems. However, the inherent constraints of IoT devices in terms of storage, computation, energy capacity, and other aspects present significant challenges to integrating blockchain into these systems. This emphasizes the necessity of developing a lightweight solution that considers these specific constraints. This conceptual article proposes a lightweight architecture based on delegated nodes, centered on blockchain technology, and an optimized practical byzantine fault tolerance (PBFT) consensus algorithm, to ensure scalability and reliability for IoT. Moreover, to reduce the storage overhead in the blockchain, an off-chain cloud-based storage solution is proposed in this article. The proposed architecture is designed to prevent direct IoT device-blockchain interactions. All system operations are defined in a single smart contract, which helps reduce the complexity and overhead of the system.
Greenhouse irrigation system based on AIoT Tariq Benahmed; Seddiki Noureddine; Benahmed Khelifa
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp531-541

Abstract

Agriculture is one of the biggest consumers of fresh water. Different types of irrigation systems are available and used in agricultural greenhouses. These irrigation systems are traditional, which do not allow for water savings and have very high maintenance costs. Smart irrigation in agricultural greenhouses is an innovative approach that contributes to a more efficient use of water in agriculture by optimizing available resources and promoting a more sustainable and profitable production. Through the use of smart technologies such as the artificial intelligence internet of things (AIoT), smart irrigation allows adjusting the amounts of water provided to the actual needs of plants, based on factors from sensors such as temperature, air and soil humidity, crop growth stages, plant conditions, and soil types. This helps to avoid water waste and reduce the risks of water stress for plants, while improving the quality and yield of crops. This article presents a smart irrigation solution in greenhouses whose results have been validated by an experimental prototype.
Hybrid plugin for detecting illicit images on the internet using EfficientNet convolutional neural networks Christine Laure Mananga; Felix Paune; Léandre Nneme Nneme
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp809-817

Abstract

The proliferation of illicit visual content on the internet, such as pornography and violent imagery, presents a growing societal concern. This paper proposes the design and implementation of a lightweight browser-integrated plugin that utilises a hybrid approach combining content based filtering with convolutional neural networks (CNNs), specifically the EfficientNetB7 architecture, to detect and block illicit images in real time. Developed using Python and TensorFlow, the plugin was trained on a curated dataset comprising NSFW, DeepNude, and safe-for-work (SFW) images. Experimental results on a dataset of 1,064 randomly selected images demonstrated a detection accuracy of 99%, with a processing time of 92 seconds and a 7% combined false positive and false negative rate. The plugin is compatible with Chrome browsers and contributes to safer online experiences, particularly for children, educators, and users in sensitive environments.
Meta-stacking models for electricity load forecasting in West Java Denanda Aufadlan Tsaqif; Bagus Sartono; Hari Wijayanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp442-453

Abstract

Indonesia’s electricity demand continues to increase due to population growth, urbanization, and industrial expansion, therefore making accurate load forecasting is essential to maintain supply-demand balance. However, electrical load demand in West Java has a complex pattern (seasonality, nonlinear behavior, weather variability, and holiday effects), which motivates the use of a meta-stacking approach to effectively capture such complexity. Previous research shows that meta-stacking outperforms individual models, but it fails to capture sudden changes and its performance consistency remains unclear. Therefore, this study proposes a meta-stacking framework for daily electricity load forecasting in West Java (2006-2023) that includes weather and holiday variables by combining CNN-BiLSTM, CNN-BiGRU, and Windowed-XGBoost forecasts through linear regression and evaluates its performance across five data-splitting scenarios and nine forecast horizons, which represents the main novelty in this research. Meta stacking shows strong generalization across scenarios and strong long-term forecasting performance across horizons, while consistently providing a balanced trade-off between MAPE and trend accuracy, where the model trained on the longest historical dataset achieves the best performance with 1.89% MAPE and 86% trend accuracy. The proposed approach successfully captures seasonal and holiday-related load patterns, indicating its potential to support PLN in improving demand planning and operational decision making.

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