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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 21 Documents
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Search results for , issue "Vol 13, No 3: September 2025" : 21 Documents clear
FOC-Based Soft Start of Induction Motors Using Trigonometric S-Curve Can, Do Van; Van Hieu, Hoang
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6656

Abstract

This paper presents a novel approach to improving the starting performance of three-phase induction motors by integrating an optimized S-curve acceleration profile based on trigonometric functions into a Field-Oriented Control (FOC) framework. Unlike conventional third- and fifth-order polynomial trajectories that suffer from limited jerk continuity and insufficient mechanical damping, the proposed method ensures smooth transitions in acceleration and jerk using sinusoidal functions. The core contribution of this work lies in the development and application of a second-order continuous trigonometric velocity trajectory that significantly reduces mechanical shocks and current oscillations during motor startup and stop phases. Furthermore, the method is designed for real-time implementation on FPGA hardware, enabling high-resolution pulse-width modulation (PWM) suitable for embedded motion control systems. Simulation and experimental results demonstrate superior motion smoothness, improved torque tracking, and enhanced mechanical reliability compared to traditional methods. This research provides a practical and effective solution for applications requiring precise soft-start/stop capabilities, particularly in elevator systems and other high-performance industrial drives.
Angularly Stable, Transparent and Flexible Modified Octagonal Shaped Frequency Selective Surface (FSS) for Sub-GHz 5G Applications Kumar, Gotte Ranjith; S, Swetha; S, Deepan; S, Loganathan; A, Muthu Manickam; S, Lavanya
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6747

Abstract

This paper offers a newly size-reduced Frequency Selective Surface (FSS) featuring band-stop behavior at 4.2 GHz. This developed FSS includes a mushroom-shaped arm with an octagonal patch. The patch is extensively adjusted by incorporating further mushroom-shaped arms, leading to a lower resonance and wider bandwidth. The designed FSS is made up of just a 11 × 11 mm unit cell on a flexible acrylic substrate that is 1 mm thick. The proposed FSS had a 1 GHz bandwidth with a centre resonance frequency of 4.2 GHz. Due to the distinct polarization behavior of this FSS, the Transverse Magnetic (TM) and Transverse Electric (TE) modes are unique and have a steady angular property up to 45º. Measurements of S-parameters for TE and TM polarizations have been validated experimentally over the 2–8 GHz frequency spectrum for both normal and oblique incident angles up to 45°. Excellent agreement between the measured and simulated data is demonstrated, verifying the FSS performance with a frequency variation of less than 3% and preserving constant band-stop properties across all measured orientations. It may be appropriate for incorporation into applicable clothes in a variety of areas because of its simplicity and ease of fabrication.
Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms Mary, Ogundele Oluwafeyisayo; Kennedy, Okokpujie; King-David, Maha Ojimaojo; Ijeh, Adaora P.; Okokpujie, Imhade P.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.5890

Abstract

Cyber threats continue to grow in this era since the bad actors are attempting to exploit individuals, organisations, and systems. The latest development in artificial intelligence has unleashed strong agents at the fingertips of humanity. As open as it is, it has made more room for possible bad actors. Systems that can successfully counter these threat actors need to be created to rescue humanity. In this research work, RNN and Random Forest classifiers' hybridised models are combined for the development of a Network Intrusion Detection System (NIDS) based on the benchmark dataset (CICIDS 2017) The requirement for an efficient and accurate method to detect network intrusions, both known and zero-day anomalies, is the primary problem considered. This research aims to enhance the accuracy and reliability of intrusion detection systems through a hybrid modelling approach. For evaluating the performance of the proposed model, various measures like accuracy, precision, recall, F1 measure, true positive rate, and true negative rate were employed. The hybrid model showed very good results with testing accuracy of 96.08%, precision of 96.0%, and recall of 96.0%, along with an F1 measure of 96.0%. The result of the experiment indicates that the model is effective and, when implemented, can detect and classify cyberattacks in modern environments.
Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data Hossain, Md. Alomgir; Begum, Momotaz; Akhtar, Md. Nasim
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6477

Abstract

Drought prediction is crucial for effective water resource management, particularly in regions prone to frequent droughts, such as Rajshahi, Bangladesh. This study presents a novel approach to quantifying and predicting drought conditions in Rajshahi, Bangladesh, utilizing machine learning models with the Standardized Precipitation Evapotranspiration Index (SPEI) as drought indices. We utilized monthly meteorological data (temperature, precipitation, humidity, wind speed, number of sunshine hours, cloud cover, potential evapotranspiration, and the climatic water balance) from 1965 to 2022. To train machine learning models, SPEI drought indicators were numerically encoded and classified into categorical drought situations. To forecast drought conditions in the Rajshahi region, we tested a variety of individual classification and regression algorithms, including Gradient Boosting, XGBoost, Multi-Layer Perceptron (MLP), Random Forest, Logistic Regression, Support Vector Machines, CatBoostClassifier, and Decision Trees. These models performed differently, with accuracy rates ranging from 85% to 88% for classification tests and R² scores from 0.25 to 0.71 for regression tasks. To increase forecast accuracy, we created two hybrid models: the Multi-Model Drought Forecaster and the Drought Anticipation Super Model. The "Multi-Model Drought Forecaster," which combines MLP, Random Forest, Gradient Boosting Classifier, and Decision Tree Classifier, obtained 92% accuracy. The "Drought Anticipation Super Model," incorporating Random Forest, Gradient Boosting, Decision Trees, Support Vector Regression, and CatBoost Classifier, increased the accuracy to 96%. The hybrid model's improved performance demonstrates that it can give more accurate and reliable drought forecasts in the Rajshahi region. These findings improve drought management strategies in Bangladesh and other climate-vulnerable areas. This study also created advanced hybrid machine learning models for drought forecasting in Rajshahi, Bangladesh, with the help of 58 years of meteorological data from 1965 to 2022 and SPEI indices. The “Multi-Model Drought Forecaster” is 92% accurate by utilizing MLP, Random Forest, Gradient Boosting, and Decision Trees. The “Drought Anticipation Super Model” is 96% accurate by adding Support Vector Regression and CatBoost Classifier to provide a better drought forecast to manage water resources effectively.
Blind Image Quality Metric for Color Images Based on Human Vision System and Deep CNN Altinbas, Ali Erdem; Yalman, Yıldıray
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.5460

Abstract

This article introduces a novel blind image quality metric (BIQM) for color images which is designed taking into account human visual system characteristics. The BIQM has a four-stage framework: RGB to YUV transformation, denoising with convolutional neural network , quality evaluation, and weighting to make it compatible with the human visual system. Experimental results, including Spearman's rank-order correlation coefficient, confirm BIQM's effectiveness, particularly in scenarios involving white noise and its compatibility with the human visual system. Furthermore, a survey involving 100 participants ranks images based on three distinct qualities, validating the method's alignment with the human visual system. The comparative analysis reveals that the proposed BIQM can compete with commonly used non-referenced quality measures and is more accurate than some of them. The MATLAB codes for the development of the BIQM are made available through the provided link: are available in the link: https://bit.ly/49MrbFX
A Hybrid Deep Learning Framework for Accurate Polycystic Ovary Syndrome Detection Using Ultrasound Imaging Boobalan, A.; Sudhakar, P.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6565

Abstract

Polycystic Ovarian Syndrome (PCOS) is a hormone-related health condition in women, commonly classified as an endocrine disorder. It is most prevalent during the childbearing years, typically between the ages of 15 and 44. PCOS leads to hormonal imbalances that cause irregular menstrual cycles, hair loss, and other symptoms, and it is associated with long-term health risks such as heart disease and diabetes. Recent advances in deep learning have shown promising results in accurately recognizing and differentiating ovarian cysts from other ovarian tumours. This study proposes a novel technique for PCOS symptom detection by analysing ovarian images through feature extraction, classification, and metaheuristic-based optimization. Ovarian images are first pre-processed for noise removal and smoothing, followed by feature extraction and classification using a Convolutional Wavelet Attention Neural Network with a Naïve Bayes Fuzzy Autoencoder (CWANN–NBFA). Optimization is then performed using the Metaheuristic Multilevel Hawks Algae Optimization (MMHAO) algorithm. Experimental evaluations were conducted on multiple ovarian image datasets. The proposed technique achieved an accuracy of over 98% across the PCOSUSG, KFHU, and MMOTU datasets, demonstrating its robustness and effectiveness in addressing the challenges of PCOS detection.
Enhanced Multi-Class Pulmonary Disorder Detection Using Hard Voting Ensemble of CNN Models on X-Ray Images Ebeid, Ebeid Ali; Youness, Farida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6606

Abstract

Lung diseases represent a major public health concern, requiring timely and accurate diagnosis. Chest X-rays are widely used for initial screening, but manual interpretation is time-consuming and subject to variability among radiologists. To address these challenges, this study presents an automated deep learning-based framework for multi-class lung disease detection. The proposed approach integrates five convolutional neural network (CNN) architectures—EfficientNetB0, DenseNet201, ResNet50, MobileNetV2, and InceptionV3—within a hard-voting ensemble classifier to improve diagnostic performance. Transfer learning is applied to extract deep features from chest X-ray (CXR) images, and the ensemble strategy enhances overall accuracy compared to individual models. The system was evaluated into six categories, including normal, COVID-19, tuberculosis, opacity, bacterial pneumonia, and viral pneumonia. Results demonstrate that the ensemble achieves approximately 97% accuracy, outperforming current state-of-the-art methods. Furthermore, the model shows strong capability in differentiating bacteria from viral pneumonia, underscoring its potential as a reliable tool for automated lung disease diagnosis in clinical practice.
Enhancing Intrusion Detection Systems in Cloud Computing Environments: A Hybrid Machine Learning Approach Alharbi, Suliman; Omer, Majzoob K.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6713

Abstract

Intrusion Detection Systems (IDS) are essential for maintaining the security of cloud computing environments, which are increasingly targeted by sophisticated cyber-attacks. This paper presents a novel hybrid approach for intrusion detection in cloud environments, combining Random Forest for feature selection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Transformer networks for contextual learning. Evaluated on CICIDS2017 and CSE-CIC-IDS2018 datasets, the proposed approach achieved weighted F1-scores of 97% and 99% respectively, significantly outperforming baseline models. The hybrid model improved accuracy from 95.1% to 98.0% and F1-score from 94.2% to 97.0% compared to LSTM-only approaches. While excelling at detecting common attack patterns such as Distributed Denial of Services (DDoS), challenges remain in identifying rare threats including SQL Injection. This research contributes to cloud security advancement by demonstrating the effectiveness of hybrid machine learning architectures in addressing the unique challenges of intrusion detection in distributed cloud infrastructures.
CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification Venkatramulu, S.; Guttikonda, John Babu; Reddy, Desidi Narsimha; Reddy, Y. Madhavi  ; Sirisha, M.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.6912

Abstract

In modern network environments, securing systems from newly emerging attacks is essential, and a constructive approach is the use of an IDS (Intrusion Detection System). When faced with attacks that are not in the list of predefined patterns, traditional IDS methods such as signature-based detection or standalone machine learning models may not function properly to detect such attacks because they are not adaptable and not designed to deal with this type of attack. The current IDS systems that employ deep-learning architectures have enhanced detection capabilities; however, most prior art systems are limited by partial feature learning, which only learns features of either spatial or temporal traffic structures. Meanwhile, the lack of contextaware mechanisms, such as attention, limits their ability to attend more to the most informative network components, leading to suboptimal detection performance and generalization. To counter this issue, in this work, we introduce CyberShieldDL, which is the first deep learning-based IDS framework with a novel hybrid architecture: IntruNet-Hybrid, combining Convolutional Neural Networks (CNN) for spatial pattern extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential feature extraction, and an attention mechanism to learn the salient features for intrusion detection dynamically. To create the framework, an optimized preprocessing and feature selection pipeline is presented to effectively and costeffectively prepare the model input. Extensive experiments on the CICIDS2017 dataset demonstrate that CyberShieldDL consistently outperforms the state-of-the-art, achieving an overall accuracy of 98.35% and high precision, recall, and F1-score in various attack scenarios. Cross-dataset validations on NSL-KDD and UNSW-NB15 also verify the system's generalization. The design provides a scalable and flexible solution for realworld network security, offering the flexibility and adaptability necessary to enhance classification accuracy and robustness against evolving attack patterns. Its modular construction enables us to extend it for real-time deployment and future adversarial robustness easily.
Supporting Communication for Deaf People with Sign Language Recognition Using Deep Learning Approach Ho, Thien; Tran, Quyen; Nguyen, Tra; Tran, Nha; Tran, Huy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.5780

Abstract

Sign language recognition (SLR) plays a crucial role in improving communication for deaf individuals. This paper investigates the recognition of sign language through deep learning models based on action features using Skeleton data from the Argentinian Sign Language (LSA64) dataset. The models explored include Multi-layer Perceptron (MLP) Neural Network, and Long Short-Term Memory (LSTM). The MLP Neural Network, utilizing multiple layers of perceptrons, reached an accuracy of 96.10%. The LSTM model, excelling in processing sequential data, attained the highest accuracy at 98.60%. These results demonstrate the effectiveness of deep learning models in sign language recognition, with LSTM showing the most promise due to its ability to effectively capture temporal dynamics. Consequently, this study opens up prospects for applying sign language recognition technology in practice, contributing to enhancing the quality of life for deaf individuals.

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