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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 : -
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Articles 9,138 Documents
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SDN multi-access edge computing for mobility management Lakkaiah, Sri Ramachandra; Kumbhinarasaiah, Hareesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1846-1854

Abstract

In recent trends, multi-access edge computing (MEC) is becoming a realistic framework for extensive social networking. The rapid proliferation of internet of things (IoT) devices has led to an unprecedented increase in data generation, placing significant strain on conventional cloud computing infrastructure. MEC also supports ultra-reliable and low latency communications (URLLC) by delivering information and computational resources more quickly to mobile users. As a result, the need for low-latency and reliable communication has become paramount. This paper proposes an MEC architecture that integrates software defined networking (SDN) and virtualization techniques, where MEC enables the orchestration and organization of mobile edge hosts (MEH). Furthermore, the proposed MEC-SDN design minimizes latency while ensuring consistent ultra-low latency communications. The result analysis clearly demonstrates that the proposed MEC-SDN model achieves latency of 6-14 ms, bandwidth of 5.2 Mbits/sec, and SDN-BWMS of 5.4 Mbits/sec, outperforming the existing SDN-Mobile Core Network model. Mobile edge systems are enabled in this research to provide mobility support for users.
Enhancing Qur'anic recitation through machine learning: a predictive approach to Tajweed optimization Daoud, Mohamed Amine; Hadjar Kherfan, Nayla Fatima; Bouguessa, Abdelkader; Mokhtar Mostefaoui, Sid Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1562-1570

Abstract

The human voice is a powerful medium for conveying emotion, identity, and intellect. Arabic, as the language of the Qur'an, holds deep spiritual and linguistic importance. Reciting the Qur'an correctly involves following Tajweed rules, which ensure phonetic precision and aesthetic quality. However, mastering these rules is challenging due to complex pronunciation and articulation variations, often requiring expert guidance. Traditional learning methods lack personalized feedback, making it difficult for learners to identify and correct errors. With the rise of machine learning, new opportunities have emerged to support Qur’anic recitation through intelligent analysis of Tajweed patterns and error prediction. This study presents a predictive model that identifies Qur’an reciters using ensemble learning techniques. By incorporating deep learning models like gated recurrent units (GRUs), long short-term memory (LSTM), and recurrent neural network (RNN), the system effectively captures the vocal features unique to each reciter. The model achieves an accuracy rate of 88.57%, demonstrating its potential to support Qur’anic learning and preservation. Nonetheless, its performance may be affected by audio quality and limited training data diversity. To improve adaptability and robustness, future work will focus on enriching the dataset and optimizing the model to generalize better across a broader range of reciters.
Adaptive deep learning framework for multi-scale plant disease detection Gadag, Tejashwini C.; Raja, D. R. Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1976-1989

Abstract

Plant disease detection is a critical task in modern agriculture, directly impacting crop yield, food security, and sustainable farming practices. Traditional methods rely on expert visual inspection, which is time-consuming, inconsistent, and inaccessible in remote areas. This study introduces an advanced deep learning (DL) framework, the adaptive multi-scale convolutional network (AMS-ConvNet), optimized for accurate and efficient plant disease identification. hierarchical feature extraction network (HFEN) integrates the multi-domain attention framework (MDAF) and adaptive scale fusion module (ASFM) to enhance feature extraction and address challenges such as complex natural backgrounds, non-uniform leaf structures, and varying environmental conditions. The proposed framework employs pre-trained knowledge adaptation (PTKA) techniques to improve generalization and overcome data scarcity. Comprehensive evaluations on multiple datasets demonstrate the model's better performance, achieving state-of-the-art metrics in precision, recall, F1-score, and accuracy. Furthermore, this approach ensures scalability and adaptability, making it suitable for real-field conditions. The study emphasizes the importance of robust, automated solutions in minimizing crop losses, reducing labor costs, and enhancing agricultural sustainability through precision disease management.
Inertia factor and crossover strategy based particle swarm optimization for feature selection in emotion classification Byreddy, Shilpa Somakalahalli; Revanna, Shashikumar Dandinashivara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1704-1713

Abstract

Emotion recognition using electroencephalography (EEG) is a better choice because it can’t be easily mimicked like facial expressions or speech signals. The emotion of EEG signals is not the same and vary from human to human, as everyone has different emotional responses to similar stimuli. Existing research has achieved lesser classification accuracy as it relies on whole feature subsets that include irrelevant features for classifying emotions. This research proposes the inertia factor and crossover strategy (IFCS)-based particle swarm optimization (PSO) algorithm to select relevant features for classification, which removes irrelevant features and enhances classification performance. Then, the self-attention with gated recurrent unit (SA-GRU) method is developed to classify the valence and arousal emotion classes, which focuses much on the significant parts of emotions and reaches high classification accuracy. The proposed IFCS-PSO and SA with GRU method achieved an accuracy of 98.79% for the valence class and 98.03% for the arousal class of the DEAP dataset, outperforming traditional approaches such as convolutional neural networks (CNN).
A solar PV-fed MF-DVR for compensation of grid-islanding issues and power-quality issues in grid-connected distribution system Bhavani, Tharinaematam; Rajababu, Durgam; Irfan, Md Mujahid
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1480-1488

Abstract

Difficulties with the quality of power come up as an effect of the inte-conneted renewable energy through grid called as distribution generation (DG) scheme. The voltage harmonics and swell-sag are happened in the utility grid as a result of power quality issues, affecting end-level consumers. Moreover, grid islanding issues is considered the most affected problem in distribution system for affecting the uninterrupted energy-flow to respective load demand. The main aim of this paper provides affective designing of the suitable cost-effective multi-functional dynamic voltage restorer (MF-DVR) has been proposed for resolving the problems. The major objective is mitigation of voltage-interruptions during grid-islanding, voltage-sag, voltage-swell and voltage-harmonics, any voltage quality in the utility grid, by utilizing the solar photovoltaic (PV) integrated MF-DVR as DG scheme through synchronous reference frame (SRF) control theory. Also, it can regulate the voltage and phase of the distribution system during sudden voltage interruptions occurred in grid-islanding. The performance of the proposed SRF controlled MFDVR for power-quality (PQ) improvement and DG integration during grid-islanding has been validated via Matlab/Simulink computing tool; the simulation findings are shown with an appealing comparison analysis.
Sensitivity-based approach for evaluation and enhancement of available transfer capability using FACTS devices Sureban, Manjula S.; Ankaliki, Shekappa G.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1431-1440

Abstract

Available transfer capability (ATC) plays an important role in the reliable and secure power system operation. It measures the transfer capability available in the transmission system for further trading over and above existing commitments without violating the system limits. The increased demand for electric power in recent years due to increasing population, automation in industries, and use of electricity in transportation, and also the deregulation of power systems results in an overload of the transmission network and hence congestion in the system. Therefore, quick and accurate calculation of ATC and its enhancement is needed for secured and reliable operation. It is possible to enhance ATC by placing the flexible alternating current transmission systems (FACTS) devices of appropriate size and at optimal locations in the system. In this paper, a computationally efficient sensitivity-based approach for evaluation and enhancement of available transfer capability in the presence of of FACTS devices is presented. The developed approach is implemented on the IEEE 14 bus system.
Extended Kalman filter based unconstrained model predictive control of a complex nonlinear system: the quadruple tank process Zidane, Zohra
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1553-1561

Abstract

This paper proposes the model predictive controller (MPC) based on the Kalman filter for a complicated nonlinear system—the quadruple tank process (QTP). The control of a multivariable and nonlinear system like a QTP is a difficult job. A number of nonlinear design techniques are implemented to ameliorate the pursuit performance of the QTP, however, the nonlinear techniques make implementation composite and computationally unsuitable. In this work, an unconstrained MPC is planed for the QTP experiences and it is controlled for both minimum and non-minimum sentence configurations in order to follow the wanted track. Its performance can be damaged once system is pass from minimum to non-minimum phase region and inversely. The unknown states required for model predictive control design are rebuilt using an extended Kalman filter. The design of model predictive control and extended Kalman filter is based on the QTP and the achievement of the proposed controller is checked for the monitoring of references. All results of simulation are affected using the MATLAB software. The results of the simulation show the capability and power of the suggested controller in respect of monitoring the trajectory and state estimation.
Characterization of binarized neural networks for efficient deployment on resource-limited edge devices Narayana, Ramya Banavara; Singh, Seema
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1815-1825

Abstract

This paper delves into binarized neural networks (BNNs) tailored for resource-constrained edge devices. BNNs harness binary weights and activations to amplify efficiency while upholding accuracy. Across diverse network configurations, BNNs consistently outshine traditional neural networks (NNs). A pioneering BNN architecture is developed in LARQ, achieving an impressive. 61% accuracy on the MNIST dataset through binary quantization, weight clipping, and pointwise convolutions. Implementation on the Xilinx PYNQZ2 FPGA board shows far quicker classification rates, with a maximum inference time of 0.00841 milliseconds per image, approximately 10,000 images being classified in this length of time. The time taken per image represents approximately 0.01% of the total inference time. This underscores BNNs' potential to redefine real-time edge computing applications. The paper makes significant strides by elucidating BNNs' performance superiority, proposing an innovative architecture, and validating its prowess through real-world deployment. These findings underscore BNNs as agile, high-performance models primed for edge computing, fostering a new era of real-time processing innovations.
UniMSE: a unified approach for multimodal sentiment analysis leveraging the CMU-MOSI Dataset Basu, Miriyala Trinath; Saha, Mainak; Gupta, Arpita; Hazra, Sumit; Fatima, Shahin; Sumalakshmi, Chundakath House; Shanvi, Nallagopu; Reddy, Nyalapatla Anush; Abhinav, Nallamalli Venkat; Hemanth, Koganti
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2032-2042

Abstract

This paper explores multimodal sentiment analysis using the CMU-MOSI dataset to enhance emotion detection through a unified approach called UniMSE. Traditional sentiment analysis, often reliant on single modalities such as text, faces limitations in capturing complex emotional nuances. UniMSE overcomes these challenges by integrating text, audio, and visual cues, significantly improving sentiment classification accuracy. The study reviews key datasets and compares leading models, showcasing the strengths of multimodal approaches. UniMSE leverages task formalization, pre-trained modality fusion, and multimodal contrastive learning, achieving superior performance on widely used benchmarks like MOSI and MOSEI. Additionally, the paper addresses the difficulties in effectively fusing diverse modalities and interpreting non-verbal signals, including sarcasm and tone. Future research directions are proposed to further advance multimodal sentiment analysis, with potential applications in areas like social media monitoring and mental health assessment. This work highlights UniMSE's contribution to developing more empathetic artificial intelligence (AI) systems capable of understanding complex emotional expressions.
Optimizing timing closure and enhancing efficiency in RTL design: a focus on physical design tasks for I2C design blocks Ramegowda, Madhura; Hirebasur Krishnappa, Krutthika; Yamadur Venkatesh, Divyashree; Sreenivasa, Kokila
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1525-1540

Abstract

Achieving precise timing closure in integrated circuit (IC) design is a significant challenge, especially with today's rapid technology advancements and intricate design specifications. Even with intense post-synthesis optimization, timing violations persist particularly in multi-corner, multi-mode designs. This research work emphasizes the necessity for power-efficient methods and streamlined approaches to boost timing closure and physical verification. Modern IC design thrives on effective physical design optimization strategies, usually tackled top-down. Clock tree synthesis (CTS) is transformative which effectively addresses clock deviation, latency, transition time, and insertion delay. This investigation mainly focuses on improving timing closure for inter integrated circuit (I2C) design blocks using custom-designed ccopt_spec and mmmc.tcl files to support multi-corner, multi-mode settings and significantly reduces register-to-register path violations from 80 to. 0. Additionally, the development and the usage of mmmc.tcl and global files are highlighted as critical components in the design process.

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