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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 9,138 Documents
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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.
A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province Winarno, Bowo; Warsito, Budi; Surarso, Bayu
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp258-269

Abstract

Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
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.
An enhanced NLP approach for BI-RADS extraction in breast ultrasound reports using deep learning Sahl, Ahmed; Hasan, Shafaatunnur; Aboghazalah, Maie M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp191-199

Abstract

Breast cancer stands as one of the top causes of death around the globe, making the accurate interpretation of breast ultrasound reports vital for early diagnosis and treatment. Unfortunately, key findings in these reports are often buried in unstructured text, complicating automated extraction. This study presents a deep learning-based natural language processing (NLP) approach to extract breast imaging reporting and data system (BI-RADS) categories from breast ultrasound data. We trained a recurrent neural network (RNN) model, specifically using a BiLSTM architecture, on a dataset of reports that were manually annotated from a hospital in Saudi Arabia. Our approach also incorporates uncertainty estimation techniques to tackle ambiguous cases and uses data augmentation to boost model performance. The experimental results indicate that our deep learning method surpasses traditional rule-based and machine-learning techniques, achieving impressive accuracy in classification tasks. This research plays a significant role in automating radiology reporting, aiding clinical decision-making, and pushing forward the field of breast cancer research.
Ultra-high isolation dual-port circular patch antenna at 2.4 GHz Boucif, Meriem; Bousalah, Fayza; Benosman, Hayat
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp140-152

Abstract

Reliable wireless communication in the 2.4 GHz industrial, scientific, and medical band increasingly relies on antenna systems that can provide high inter-port isolation in multiple-input multiple-output (MIMO) configurations. This paper presents a circular microstrip patch antenna and its extension to a dual-port MIMO configuration designed for 2.4 GHz operation. The antenna is implemented on a low-loss substrate and evaluated using full-wave electromagnetic simulations to assess impedance matching, radiation performance, and MIMO diversity metrics. To enhance inter-port isolation in the array, an inverted U-shaped defected ground structure (DGS) is introduced between the two radiating elements. The optimized design achieves excellent matching around 2.4 GHz and ultra-high isolation of approximately -78.7 dB, while maintaining stable gain and radiation patterns across the operating band. These results indicate that the proposed antenna offers a simple and effective solution for compact, energy-efficient, and robust 2.4 GHz MIMO front ends in internet of things (IoT) and other shortrange wireless communication 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.
Detection of COVID-19 using chest X-rays enhanced by histogram equalization and convolutional neural networks Tchagafo, Nazif; Ez-Zahout, Abderrahmane; Belaid, Ahiod
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp387-393

Abstract

The persistent global health crisis initiated by the COVID-19 pandemic continues to demand robust and high-throughput diagnostic solutions. While gold-standard methods, such as polymerase chain reaction (PCR) testing, are accurate, their scalability and turnaround time remain limitations in high volume settings. This paper introduces a novel deep learning framework designed for rapid and accurate detection of COVID-19 from chest X-ray (CXR) imagery. Our methodology leverages a convolutional neural network (CNN) architecture, augmented by a crucial pre-processing stage: histogram equalization. This step is vital for enhancing the subtle contrast features inherent in CXR scans, there by significantly improving the quality of the input data and facilitating superior feature extraction by the CNN. The model was trained and rigorously validated on a dedicated dataset. Performance was systematically quantified using a comprehensive confusion matrix, yielding key metrics such as precision and specificity, alongside the receiver operating characteristic (ROC) curve. The achieved results are highly encouraging, demonstrating a classification accuracy of 98.45%. This innovative approach offers a substantial acceleration of the diagnostic process, providing a non-invasive and highly effective complementary tool for clinicians. Ultimately, this advancement has the potential to streamline patient management protocols and alleviate diagnostic pressures on global healthcare infrastructures.
SCADE: a deep learning ensemble for semantic flow analysis in smart contract vulnerability detection Srirama, Muralidhara; Banavikal Ajay, Usha
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.pp1417-1429

Abstract

A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Effective medium ratio obeying metamaterial absorber for 5G sub-7 GHz and sub-8 GHz applications Jakir Hossain, Mohammad; Uddin, Md. Alim; Islam, Md. Mesbahul; Ghosh, Keya; Jahan, Nusrat; Akter Asma, Mukta
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.pp1368-1376

Abstract

Metamaterials possess the capability to enhance fifth-generation (5G) communication technology. This article proposes an innovative construction of a miniature metamaterial absorber (MMA) with a dramatically improved effective medium ratio (EMR) characterized by utilizing a multi-square split-ring resonator (MSSRR) MMA unit cell specifically designed for operation in the 5G sub-7 GHz and Sub-8 GHz frequency bands. The unit cell of the MMA is designed using a commercially available FR-4 material with εr=4.3, which is cost-effective. The proposed MMA achieves a remarkably high EMR of 9.83, indicating superior compactness and design efficiency. The MMA of interest operates with absorbance peaks of 70.632%, 96.936%, and 79.930% within the frequencies of 3.554 GHz, 4.940 GHz, and 8.335 GHz, respectively. Along with the absorption analysis, our examination also includes E-field, H-field, surface current, and power flow. The expected MMA has proven potential for application in some frequency bands related to 5G, released absorption signal, and specific absorption rate (SAR) assistance.

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