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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
 927   928   929   930   931 
Multi-objective task scheduling in large-scale distributed systems using a Lévy flight-based hybrid Bat-Whale optimization algorithm Ali Mohammed Ahmed; Manar Younis Kashmola
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.pp913-926

Abstract

The rapid growth of cloud computing demands efficient task scheduling strategies capable of handling heterogeneous resources, dynamic workloads, and multiple conflicting objectives. Existing approaches often optimize a single criterion, limiting their effectiveness in large-scale distributed systems. This paper proposes hybrid Bat–Whale optimization algorithm (BWOA), a hybrid scheduling algorithm combining the Bat algorithm and Whale optimization algorithm, enhanced with Lévy flight-based exploration, adaptive crossover, and a smart local search mechanism. The framework balances global exploration and local exploitation while preserving population diversity and intensifying search around promising solutions. A problem-aware local search reallocates long-duration tasks to high performance virtual machines and selectively swaps tasks with poor response times. Experiments on a heterogeneous cloud environment with 300 tasks and 50 virtual machines, using min–max scaling for workload normalization, demonstrate that BWOA outperforms classical methods, including first come, first served (FCFS) and Min-Min scheduling algorithms, achieving superior makespan (≈32.77 s) while maintaining competitive utilization, throughput, and energy efficiency. These results highlight the effectiveness of hybrid metaheuristic approaches integrating multiple optimization strategies for multi-objective task scheduling in large scale cloud systems, providing a robust and scalable solution for both academic research and practical deployment.
Region of confidence measurement for the purpose of localization in wireless sensor networks Anisur Rahman; K. M. Safin Kamal
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.pp380-390

Abstract

Accuracy of localization of the sensor nodes is the key factor for the reliability and efficiency of wireless sensor networks (WSNs). Although a number of localization algorithms have been proposed for WSNs, the major challenge for most real-world applications is the uncertainty of measurements of the references. The proposed work describes a novel approach for estimating the localization accuracy with the help of the region of confidence (ROC), the common region formed from the intersections of the circular regions defined by the distances of numerous reference nodes in a 2D plane. The work investigates the feasibility of localization accuracy with the help of simulations performed for different scenarios with varying distances of the reference nodes. The proposed approach utilizes the ROC technique for providing a measurable significance for the evaluation of localization efficiency for a range of error conditions. This proposed work contributes towards the evolution of efficient localization techniques for real-world environments of WSNs.
Analysis of OFDM and filter bank multicarrier with offset quadrature amplitude modulation for 5G communication: a comparative study Magda Yousef Mοhаmеd; Esraa M. Eid; Mohammed Abo-Zahhad; Ahmed Hassan Еldеib
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp127-138

Abstract

Emerging applications for 5G and beyond require wireless communication systems with high spectral efficiency, low latency, reliable synchronization, and robust channel estimation techniques. This paper presents a comparative analysis between orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) under identical simulation conditions. The comparison is performed in terms of spectral efficiency, power spectral density (PSD), bit error rate (BER), peak-to-average power ratio (PAPR), and channel estimation performance. Simulation results show that FBMC/OQAM has higher spectral efficiency and significantly reduced out-of-band (OOB) emissions than OFDM due to its superior spectral containment. Moreover, FBMC/OQAM provides better channel estimation performance in the frequency-selective multipath fading environment. On the other hand, OFDM has lower computational complexity and better PAPR performance. The obtained results highlight the performance differences between OFDM and FBMC/OQAM systems and demonstrate the potential of FBMC/OQAM as a promising waveform candidate for future wireless communication systems.
Deep Q learning algorithm for detecting DDoS attacks on IoT devices Lana Kamla Ahmed; Kayhan Zrar Ghafoor
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp299-313

Abstract

The rapid expansion of internet of things (IoT) networks has heightened security risks, particularly regarding distributed denial of service (DDoS) attacks against devices with limited computing capacity. High detection accuracy is crucial for these resource-constrained environments, where false positives can disrupt legitimate traffic and false negatives allow attacks to persist. However, modern reinforcement learning (RL) and machine learning (ML) intrusion detection solutions often exhibit poor generalization due to static state representations. To address this, this paper proposes a deep Q-learning (DQL) framework that integrates K-means clustering directly into the RL action space. Unlike prior RL-based IDS models, our approach dynamically integrates clustering into the learning process, enabling adaptive state representation and improved generalization to unseen traffic patterns. The system is formulated as a Markov decision process where the agent optimizes a composite reward function based on accuracy, precision, recall, and F1-score. Evaluated on the N-BaIoT dataset using 10-fold cross-validation, the proposed method achieves a classification accuracy of 98.95% and a weighted F1-score of 98.73%, significantly outperforming traditional ML and RL baselines. These results demonstrate the framework's effectiveness as a scalable, adaptive solution for intelligent IoT DDoS detection.
Dung-beetle-optimization algorithm-based P-I-D controller for a separately excited DC-motor Kerrache Soumia; Haidas Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp93-102

Abstract

Understanding the proportional, integral, and derivative (P-I-D) control system is crucial for optimizing its parameters to achieve the desired system performance. The proportional gain determines how aggressively the system responds to the error, while the integral gain helps to eliminate any steady state error. The derivative gain plays a role in stabilizing the system by damping out any oscillations caused by sudden changes in the error. P-I-D control is a widely used control technique in various engineering applications, including the control of DC-motors, which is refers to adjust the motor’s input voltage, current, speed or position in order to achieve a desired output. One approach to tuning P-I-D parameters is the ziegler nichols (ZN) methods, where the first one involves to plot the step response of the model’s open loop with its tangent line. The other method conists systematically increasing the gains until the system becomes unstable, and then adjusting the gains to find the ultimate gain and ultimate period. One of the main advantages of metaheuristic algorithms is their ability to quickly converge to near-optimal solutions without getting stuck in local optima. This is achieved by using a combination of exploration and exploitation strategies to efficiently search through the solution space. Within our study, we seek to incorporate the Dung-Beetle based optimization algorithm (DBO) to adjust the P-I-D controller for a separately excited DC-motor’s (SEDCM) speed control using MATLAB-software relies on the objective functions: the integral absolue error (IAE), the integral squared error (ISE) and the integral time absolue error (ITAE). The results obtained are compared in their best performances on rise time, settling time, overshoot, peak response and peak time.
HawkNet: an intelligent bio-inspired optimization based patch wise adaptive U-Net framework for retinal blood vessel segmentation in fundus images Saba Sheiba; Saba Sheiba
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp157-178

Abstract

The accurate segmentation of retinal blood vessels plays a pivotal role in the early diagnosis and monitoring of health issues like diabetes, high blood pressure, and glaucoma. Traditional machine learning techniques such as matched filtering, morphological processing, and edge detection have a tough time to see tiny blood vessels clearly because eye images often have uneven lighting, poor contrast, and blood vessels that twist and turn in complex ways. To overcome these challenges, this work introduces an improved Patch-wise U-Net method with Harris Hawk optimization (HHO). The patch system split fundus images into overlapping patches, enabling the network to focus on localized vessel features such as fine capillaries, bifurcations, and vessel boundaries that are often missed in global segmentation. Meanwhile, HHO is employed to fine-tune the U-Net’s hyperparameters and learning weights, achieving faster convergence and enhanced segmentation accuracy without manual tuning. To establish generalizability and reliability, the proposed framework is rigorously tested on two standard retinal image repositories DRIVE, and STARE. Experimental evaluation demonstrates significant improvement in key performance metrics, including accuracy of 97.8%, precision 95.8%, recall 96.5%, f1-score 97.3%, IoU of 96.2%, and dice coefficient (DC) 94.3%, highlighting the model’s capability to capture thin and thick vessels structures while minimizing false detections. Additionally, qualitative segmentation further confirm that the proposed framework effectively preserves the continuity and morphology of thin and thick vessels, even in regions with low contrast or uneven illumination. Overall, the proposed method visual inspection has revealed that the suggested method can segment thin and thick vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.
Electrical engineering in the era of autonomous intelligence: building sustainable, resilient, and self-evolving energy systems Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp1-6

Abstract

Electrical engineering is entering a transformative era in which autonomous intelligence is becoming an integral component of modern energy infrastructures rather than merely an auxiliary computational tool. The convergence of advanced power electronics, renewable energy technologies, intelligent sensing, edge computing, artificial intelligence, and high-speed communications is enabling electrical systems to evolve from passive and centrally controlled networks into adaptive, resilient, and self-evolving ecosystems. This editorial discusses the emerging paradigm of autonomous electrical engineering, where future power systems are expected to perceive operating conditions, learn from historical and real-time data, predict disturbances, and autonomously optimize their performance while maintaining reliability, security, and sustainability. Beyond conventional objectives such as efficiency and stability, next-generation electrical systems must address increasing renewable penetration, distributed energy resources, electrified transportation, cyber-physical security, and climate resilience. The editorial also highlights several promising research directions, including AI native power system operation, autonomous microgrids, digital twins, physics-informed intelligence, trustworthy and explainable AI, intelligent power electronic converters, and coordinated human–AI decision-making. These developments position electrical engineering as a foundational discipline for achieving sustainable development and future energy transition, while emphasizing that autonomous intelligence should augment engineering expertise to create safer, more reliable, and environmentally responsible electrical infrastructures.
MobileNetV2 with transfer learning for brain tumor classification Aziz Srai
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp281-298

Abstract

This document presents a deep learning-based approach for the automatic classification of brain tumors from magnetic resonance imaging (MRI) images, using the lightweight MobileNetV2 model combined with transfer learning and fine-tuning techniques. The study aims to address the constraints of resource-limited medical environments, where model speed and lightweight design are as important as accuracy. The dataset used is a fusion of three public databases (Figshare, SARTAJ, and BR35H), comprising 4,480 images for training and 1,600 for testing, divided into four classes: glioma, meningioma, pituitary tumor, and healthy brain. The methodology includes several steps: resizing the images to 224×224 pixels, normalizing pixel values between 0 and 1, augmenting the data through random rotations, shifts, and zooms to avoid overfitting, and then extracting features using MobileNetV2 pre-trained on ImageNet. The strategy adopted comprises two phases: first, transfer learning where only the layers added at the top of the model are trained for 10 epochs, then fine-tuning consisting of unfreezing the last 20 layers of the base model and retraining them with a reduced learning rate for 5 epochs. The results obtained show an overall accuracy of 86.56% after fine-tuning, with a macro-mean area under the ROC curve (AUC) of 0.9645, indicating excellent discriminatory power. The confusion matrix reveals that the "no tumor" class achieves perfect performance (400 out of 400), while the "meningioma" class remains the most difficult to classify, often confused with gliomas and pituitary tumors. Compared to more resource-intensive models like VGG16, ResNet50, or EfficientNet, MobileNetV2 offers an optimal balance between performance and lightweight design, with a significantly lower number of parameters, making it particularly well-suited to resource-constrained environments. The authors conclude that this approach provides reliable diagnostic support for radiologists, accelerating tumor detection without replacing medical expertise. Future directions include clinical validation on multi-center data, integration of an automated segmentation step, and exploration of newer architectures such as attention mechanisms.
Coral classification in underwater images using a dual-branch deep learning framework Pracharat Sa-ngadsup; Chawan Koopipat
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp207-218

Abstract

Automated coral classification from underwater imagery is essential for large-scale reef monitoring but remains challenging due to color attenuation, illumination variability, and differences between texture-focused close- range images and morphology-focused colony-level observations. To address these challenges, this study proposes a dual-branch convolutional neural network that integrates information from the CIELAB (LAB) color space with structural descriptors derived from the discrete wavelet transform (DWT). RGB images are first converted to the LAB color space to separate luminance and chromatic components in separate channels, enabling the model to exploit color and lightness information more explicitly. Structural information is extracted from the luminance channel using wavelet decomposition to capture high-frequency morphological patterns. The two representations are processed through parallel convolutional branches and fused at the feature level for classification. Experiments conducted on a unified coral dataset containing texture dominant and morphology-focused imagery across 14 classes show that the proposed method achieves 96.52% accuracy on the texture-focused RSMAS dataset and 88.33% accuracy on the morphology-focused structure RSMAS dataset, reducing the cross-domain performance gap from 22.46% to 8.19% compared with RGB baselines, demonstrating improved cross domain robustness for coral classification under heterogeneous underwater imaging conditions.
REHA:real-time IoT-based energy efficient home automation system using ESP8266 and PIR motion sensors Md. Kamal Ibne Sufian; Poly Bhoumik; Selina Sharmin; Nazma Tara
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp179-191

Abstract

Technological developments have improved living standards, leading to greater demand for home automation based on the internet of things (IoT) concept. This study addresses the limitations of existing home automation systems, which are often costly, complex, and lack energy-saving features. A low-cost IoT-based home automation system is developed using the ESP8266 NodeMCU and PIR motion sensors to enable both remote control and automatic operation of house hold appliances. It uses the Blynk platform for cloud-based monitoring with smart motion-based logic to minimize unnecessary power consumption. The evaluation is conducted through various controlled scenarios and an estimate based energy analysis derived from standard appliance ratings. Experimental results showed a reduction in household energy consumption of about 13–15%. The proposed system offers a cost-effective, practical approach to smart home energy management by combining affordability, automation, and measurable efficiency improvements.

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