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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 49 Documents
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Search results for , issue "Vol 40, No 3: December 2025" : 49 Documents clear
Accelerated framework for image compression and reconstruction based on compressive sensing M. Yousif, Tasneem; M. Ahmed, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1241-1250

Abstract

Image compression is a crucial field driven by advancements in communication and imaging technologies. Its primary goal is to achieve low bit rates while maintaining high-quality image reconstruction. Compression is essential in digital image processing, multimedia applications, and medical imaging. Various algorithms exist for image compression and reconstruction, each differing in efficiency. Compressive sensing (CS) algorithms, commonly used for radar data reconstruction, require iterative computations that demand significant processing power and time, limiting real-time applications. To overcome these challenges, this study proposes a parallel-pipelined processing approach to enhance compression and reconstruction efficiency. The method accelerates processing speeds, increases data throughput, and optimizes performance by reducing data size. The proposed approach divides image data into multiple parallel processing branches, significantly reducing computational cycles. This results in faster execution and improved real-time applicability. MATLAB simulations and field-programmable gate array (FPGA) hardware implementations have been conducted to validate the system’s effectiveness. The results demonstrate that the parallel-pipelined method significantly enhances efficiency compared to traditional approaches, making it suitable for applications requiring high-speed image processing, such as satellite imaging and medical diagnostics.
Development of an analysis capacity model for high electron mobility transistor AlGaN/GaN Farti, Azzeddine; Touhami, Abdelkader
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1261-1269

Abstract

In this paper, we demonstrate the analytical model developed to characterize the gate-to-drain capacitance Cgd and the gate-to-source capacitance Cgs, and the impact of the gate length on those capacitances, for the high electronic mobility transistor based on GaN. This model is developed from our previous work on the current voltage characteristic (I, V), and small signal parameters for AlGaN/GaN HEMT. The research study examined the impact of parasitic resistances (drain, source), low field mobility, the aluminum amount in the AlGaN barrier, and high-speed saturation. The developed model has matched the experimental data well, confirming the validity, accuracy, and robustness of the model we have developed.
Mobile application for diagnosing alzheimer's based on clinical dementia rating Supriyanti, Retno; Putra Yubiksana, Muhammad; Mahardika Wijonarko, Bintang Abelian; Ramadhani, Yogi; Syaiful Aliim, Muhammad; Irham Akbar, Mohammad; Budi Widodo, Haris; Widanarto, Wahyu; Alqaaf, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1607-1617

Abstract

Alzheimer's is a neurodegenerative disease characterized by memory loss, impaired thinking abilities, and changes in behavior. It is the most common form of dementia, significantly affecting a person's ability to carry out daily activities. Statistics indicate that the number of individuals suffering from Alzheimer's worldwide continues to rise as the population ages. Diagnosing Alzheimer's is a complex process that typically requires a skilled medical team. One diagnostic tool that can be utilized is an MRI machine. Previous research focused on extracting features from MRI images taken from three different cross-sections: axial, coronal, and sagittal. Based on these three types of cross-sectional images, we developed a system to classify the severity of Alzheimer's. This paper focuses on creating an Alzheimer's classification system accessible through a mobile application. The results indicate that our system has a performance accuracy of 90% in classifying the severity of the disease.
Efficient PAPR reduction technique in OFDM system using amplitude clipping and selective filtering M., Supriya; R., Sukumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1308-1316

Abstract

One of the most important transmission methods for the next generation of wireless communication systems is orthogonal frequency division multiplexing (OFDM). Transmitting an OFDM signal in a noisy environment with a low bit error rate (BER) is the primary goal. High peakto-average power ratio (PAPR) at the transmitter, which lowers the transmission peak power, is one of OFDM's biggest drawbacks. In this paper, we propose efficient PAPR reduction technique in OFDM system using amplitude clipping and selective filtering. The efficient multiefficiency PAPR reduction strategies with pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) modulation are employed with selective filtering and evaluated in terms of percentage reduction level to lowest PAPR of 3.841 db. It is observed that QAM modulation produces better results compared to PAM modulation with less BER of 0.003 for signal-to-noise ratio (SNR) of 20 db.
Hybrid TCP SYN attack detection model in SDN Muzafar, Saira; Zaman Jhanjhi, Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1345-1356

Abstract

Software defined network (SDN) is a developing concept that emerged recently to overcome the constraints of traditional networks. The distinguishing characteristic of SDN is the uncoupling of the control plane from the data plane. This facilitates effective network administration and enables efficient programmability of the network. Nevertheless, the updated architecture is susceptible to cyberattacks including distributed denial of service (DDoS) attacks, that can impair network regular functions and hinder the SDN controller from assisting authorized users. This paper introduces hybrid deep learning model, to detect DDoS assaults triggered by TCP SYN attacks in SDN environments. Our proposed model integrates a temporal convolutional network (TCN) with a stacking classifier that leverages logistic regression, which is an innovative hybrid approach. We assessed the performance of our model by utilizing the benchmark CICDDoS2019 dataset. When compared to other benchmarking techniques, our model significantly improves attack detection. The experimental results indicate that the proposed hybrid model attains 99.9% accuracy for attack detection compared to the available approaches.
Meta-model integration with attention mechanisms for advanced decision-level fusion in machine learning Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1325-1336

Abstract

This work proposes an advanced meta-model approach that incorporates forecasts from multiple machine learning models to improve classification accuracy in complex tasks. The approach employs decision-level data fusion, where predictions from random forest (RF), XGBoost, neural networks (NN), and support vector machine (SVM) are combined within a meta-model framework. The meta-model incorporates an attention mechanism and a gated model selection process to dynamically emphasize the most relevant model outputs based on input features. The results demonstrate superior accuracy in predicting explicit content compared to traditional fusion methods. This research highlights the potential of attention-enhanced meta-models in improving interpretability and accuracy across various domains. The integration of meta-models with attention mechanisms has the potential to significantly enhance decision-level fusion in machine learning applications. This study investigates the development of an advanced fusion framework leveraging attention mechanisms to improve decision-making accuracy in multi-source data environments. The proposed method is evaluated across multiple datasets, demonstrating its efficacy in increasing predictive performance and robustness.
Cardio meta-stack: a meta-classifier ensemble for enhanced cardiovascular disease prognosis S., Swetha; Zolgikar, Sneha; S. H., Manjula; K. R., Venugopal
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1630-1637

Abstract

Cardiovascular diseases (CVDs) remain a significant global health concern, necessitating effective preventive measures and early diagnosis to reduce mortality rates. Leveraging machine learning models to identify risk factors holds great promise, especially in cardiology. This study introduces a robust methodology for prognosing cardiac illnesses based on patient-specific factors. By integrating five publicly available datasets from the UCI Repository and employing Feature Importance techniques for optimal risk factor selection, the proposed approach enhances prediction accuracy. Furthermore, the inclusion of the density-based spatial clustering of applications with noise (DBSCAN) algorithm assists in noise detection and removal, thereby improving model precision. The proposed Cardio MetaStack model, coupled with a stacking classifier ensemble, achieved an accuracy of 94.91%, surpassing that of traditional algorithms such as XGBoost 90.45%, demonstrating its efficacy in heart disease prediction.
Optimizing YOLOv8: OpenVINO standard quantization vs accuracy-controlled for edge deployment G. Raju, Chandrakala; Devarapalli, Ajaykumar; Mahendran, Rakshitha; Madhusudan, Sathwik; Prasad, Omkar
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1567-1575

Abstract

Object detection models, such as you only look once (YOLO), are widely utilized for real-time applications; however, their computational complexity often restricts deployment on edge devices. This research investigates the optimization of YOLO models using OpenVINO, both with and without accuracy control, to enable efficient inference while preserving model accuracy. A two-step pipeline is proposed: first, YOLO models are converted into OpenVINO’s intermediate representation (IR) format, followed by the application of post-training quantization (PTQ) to reduce model size and enhance latency. Additionally, an accuracy-aware quantization approach is introduced, which maintains model performance by calibrating with a validation dataset. Experimental results illustrate the tradeoffs between standard and accuracy-controlled quantization, demonstrating improvements in inference speed while ensuring minimal accuracy degradation. This study provides a practical framework for deploying lightweight object detection models on edge devices, particularly in realworld scenarios such as autonomous systems, smart surveillance, and smart queue management systems.
QV finder: an accurate Quran verse finder system Al-Rfooh, Bashar; Abdel-Majeed, Mohammad; Al-Awawdeh, Shorouq; A. Darabkh, Khalid
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1486-1499

Abstract

A voice-based search is becoming increasingly important for accessing information across various domains. One of the most challenging areas is Quranic verse search, where precise recitation rules (Tajweed), dialectal variations, and background noise affect accuracy. In this work, we present QV finder, an artificial intelligence (AI)-powered system that utilizes a finetuned whisper-based automatic speech recognition (ASR) model specifically trained on diverse Quranic recitations for the whole Quran. In this paper, we present a robust pipeline for Quranic verse retrieval that bridges the gap between ASR technology and domain-specific linguistic complexity. The model supports both professional and normal reciters, even under noisy conditions. To enhance the localization of verses from partial recitations, we integrated tokenization and advanced string-matching algorithms such as Levenshtein distance and FuzzyWuzzy. For normal reciters, the proposed model achieves a word error rate (WER) of 10.1% and character error rate (CER) of 3.3%, outperforming Google ASR, which exhibits a WER of 27.04%, and a CER of 7.13%. The model also achieves 100% verse retrieval accuracy with a 2.5% false positive rate. Our best fine-tuned model is uploaded here: https://huggingface.co/basharalrfooh/whisper-small-quran.
Proposition of a new fitness function: Hadj-said fitness function Ali Pacha, Hana; Hadj Brahim, Abderrahmene; Lorenz, Pascal
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1669-1688

Abstract

In the dynamic field of artificial intelligence, genetic algorithms (GAs) offer a powerful approach to solving complex problems by mimicking biological mechanisms such as mutation, crossover, and natural selection. Their efficiency relies primarily on the fitness function, which evaluates the quality of candidate solutions and guides the evolutionary process toward an optimal outcome. A well-designed fitness function not only enhances convergence speed but also reduces the risk of stagnation and improves algorithmic accuracy. This paper explores the fundamental role of fitness functions in optimization, machine learning, multi-objective optimization, and cryptography, highlighting their impact on the performance of GAs. We propose a novel fitness function that incorporates the influence of crossover, mutation, and inversion rates on solution quality. This approach, which diverges from conventional models, demonstrates improved convergence behavior and adaptability across different problem domains. The proposed method enhances GA performance not only in secure data encryption but also in general optimization and learning tasks, making it a valuable contribution for both researchers and practitioners, which can open new avenues for research in the development of more robust evolutionary strategies that can adapt effectively to the specific characteristics and challenges of each problem domain.

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