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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 783 Documents
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Partial Discharge Source Identification Using Pulse Height Distribution for Diagnosis in High Voltage Rotating Machines Deshpande, Amol; Chimurkar, Priya; Rathod, Kiran; Cheeran, A. N.; Mangalvedekar, H. A.
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.v13i3.5754

Abstract

Electrical discharges that are localized in nature and do not completely bridge the electrodes are called as Partial Discharge (PD). PD is the major cause of insulation degradation, and it may eventually lead to system breakdown. Therefore, monitoring of such discharges is important considering efficient diagnosis of high voltage insulation systems. Depending on the source of discharge, there are types of discharges generally occurring in rotating machines viz. Slot Discharge, Delamination, void discharge etc. The plot of count of PD pulses versus the PD magnitude is called the PD height distribution (PDHD) plot or Pulse Height Distribution (PHD) plot. This plot is derived from the actual measurement data on high voltage (HV) rotating machines and the results thus obtained are discussed in this research work. This plot is used to identify the presence of individual PD source or simultaneous occurrence of PD sources in the HV rotating machine. This is the novel contribution of this research work. Phase Resolved PD (PRPD) patterns are used to validate the results. The results for individual discharges and simultaneous discharges are discussed in this paper. The significance of pulse height analysis for diagnosis of HV rotating machines is discussed in this paper.
AI-Powered CT Scan Enhancement: Turning CTs into MRI Quality Images for Faster and Safer Diagnoses A K, Meeradevi; Rufina P, Maria; C, Sanjana; Bhetariya, Siddharth; Kumar, Harsh; Sharma, Pranesh
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.v13i3.6996

Abstract

The use of deep learning (DL) architectures like U-Net and GANs ensures secure, distributed model training across hospitals. The proposed work uses a privacy-preserving federated learning framework for emergency neuroimaging, enabling AI models to convert Computed Therapy (CT) scans into Magnetic Resonance Imaging (MRI) equivalent images as MRI images gives more accurate soft tissue details without compromising patient data. The proposed model integrates DL with saliency maps and Grad-CAM which are the Explainable AI (XAI) tools. The idea is to offer the transparency and build trust in diagnosis of disease. The image quality is measured using the metrics Structural Similarity Index (SSIM) and Paek Signal to Noise Ratio (PSNR) which ensures high-quality image synthesis. The proposed solution enhances the diagnostic accessability in resourse limited hospitals and rural hospitals by preserving patient data with standards. The enhanced model strengthens the framework, privacy techniques and secure aggregation techniques are used to prevent model data leakage during model training or updates. The study provides additional layer of protection to ensures using Federated Learning that even gradient-level information shared between hospitals cannot be traced back to individual patient data. The proposed system is scalable and enables integration with diverse hospital infractures and imaging modalities. The model provides the accessability by turning CT to MRI through secure XAI. The model accuracy ranges to 95% remaining validation accuracy close to train accuracy. The proposed idea provides emergency diagonistics with easy accesibility by preserving privacuy.
Optimizing IoT Protocol Coexistence and Security using Software Defined Network and Intelligent Machine Learning Detection Bhai, Reshma N.; Patil, Mahadev S.
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.v13i3.6913

Abstract

The rapid growth of heterogeneous IoT environments has made seamless communication across protocols like MQTT and CoAP increasingly difficult, leading to interoperability gaps, latency issues, and security vulnerabilities. This paper proposes a Software-Defined Networking (SDN)-based architecture that integrates MQTT and CoAP through a bidirectional translation layer, while embedding machine learning (ML) intelligence for real-time flag status monitoring and Denial-of-Service (DoS) attack detection. The system leverages classifiers such as SVM, DT, NB, RF, and KNN within the SDN controller to dynamically predict operational states and mitigate malicious traffic. To evaluate performance, a Mininet-based IoT testbed with 50 heterogeneous nodes was deployed. Simulation results demonstrate that the proposed system achieves up to 95% message delivery success, reduces average latency by 18% compared to baseline translation methods, and saves 12–15% residual energy when using SVM-based classification. While the system improves interoperability and security, it also introduces computational overheads at the SDN controller due to ML inference, which may impact CPU and memory utilization in resourceconstrained environments. The proposed solution is highly relevant for smart city, industrial IoT, and healthcare applications, where interoperability and real-time resilience against attacks are critical. By unifying heterogeneous devices and enhancing security, this approach provides a scalable and practical pathway for next-generation IoT networks.
Exploratory Analysis of the Impact of Data Balancing on the Classifier’s Performance in Predicting Creditworthiness Reliability Hassan, Md. Mahedi; Hossen, Arif; Arafat, Yeasin; Sarker, Md Nurunnabi; Jamil, Md Hossain; Siddika, Ayesha
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.v13i3.6667

Abstract

This study examines the application of machine learning algorithms for creditworthiness prediction within the banking sector and addresses the issue of class imbalance through sampling methodologies. The research indicates that using the Stacking Ensemble algorithm with random oversampling can predict creditworthiness with an impressive 93% accuracy. The method consistently achieves excellent precision, recall, and F1-score values, indicating that it can produce accurate predictions while maintaining a balanced evaluation. Random oversampling helps models improve their predictive accuracy and reduce class imbalance. The research findings underscore the feasibility of this technique for financial institutions, facilitating informed lending decisions and improving credit risk assessment methodologies. This research enhances the field by identifying the most effective machine learning methods for accurate creditworthiness evaluation. Using XAI tools like Shapash provides financial organizations with valuable insights into assessing loan risks and enhancing their lending operations.
Optimized Dual-Band Reconfigurable Power Amplifier for 5G Mouad, El Kobbi; Bendali, Abdelhak; Zarrik, Samia; Habibi, Sanae; El Wardi, Reda Abid; Habibi, Mohamed
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.v13i3.5685

Abstract

This article presents the design of a dual-band power amplifier capable of operating in three different classes: A, AB, and B. Simulation results reveal that a single power amplifier can efficiently operate at two specific frequencies of the 5G core band, namely 3.5 GHz and 3.8 GHz. The amplifier demonstrates exceptional stability and matching across both frequency bands. It achieves a maximum gain of 17.7 dB, a maximum output power of 41.2 dBm, and a maximum power-added efficiency (PAE) of 70%. These performance characteristics are achieved through an innovative design that allows for frequency band reconfiguration via a PIN diode switch, as well as the selection of the operating mode among classes A, AB, and B. This flexibility makes the amplifier ideal for applications in 5G communication systems, offering an optimal balance between linearity, energy efficiency, and overall performance
Ensemble Based Machine Learning Approach for Heart Disease Prediction El Shenbary, H. A.; Hassan, Belal Z.; Elsayed, Amr T. A.; Khalaf Allah, Khaled A. A.
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.v13i3.7015

Abstract

Ensemble machine learning has developed into a strong approach for enhancing the precision and resilience of predictive models through the integration of various learning algorithms. This research presents an innovative ensemble classification framework employing a soft voting approach that combines three gradient boosting techniques XGBoost, LightGBM, and CatBoost to improve heart disease prediction efficacy. The model undergoes evaluation using four distinct datasets (Heart Attack Risk Prediction Dataset, Heart Attack Dataset, Cleveland Heart Disease dataset and Heart Disease Dataset) obtained from Kaggle and other repositories, each reflecting various populations and diagnostic variables. By implementing thorough preprocessing, careful feature selection, and even training-testing-validating splits, the system attains reliable and exceptional classification performance. Experimental findings reveal that the suggested ensemble approach greatly surpasses classic and standalone models, attaining flawless or nearly flawless accuracy on all datasets, reaching a peak accuracy of 100% on the first dataset, 98% on the second dataset, 100% on the third dataset and 98.4% on the fourth dataset. The framework's achievement underscores its viability for real world use in clinical decision support systems and emphasizes the efficiency of ensemble methods in medical diagnosis.
A Systematic Literature Review (SLR) of Mirai Botnet Compromise Detection in Internet of Things (IoT) Network Eweoya, Ibukun; Olajide, Funminiyi; Obed, Jonathan; Asante, Christian
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.v13i3.6130

Abstract

Since its invention, Mirai botnet has remained a significant concern in IoT network security. The botnet and its evolving variants are a major threat to professionals responsible for securing IoT infrastructures. The danger of the botnet is attributed to the fact that it has been utilized for the execution of numerous Distributed Denial of Service (DDoS) attacks on different network infrastructures in the past. Several researchers have proposed techniques in mitigating the effect of this botnet. This research systematically reviews existing detection techniques and evaluates how effective they are in mitigating Mirai botnet attacks between 2017 and 2024. Using PRISMA methodology, 177 articles were initially identified from Scopus, Springer Link, IEEE Xplore, and Web of Science in order to broaden the scope of the search. 27 studies passed the inclusion criteria, and were analyzed thereafter. Findings reveal a predominant reliance on AI-driven detection methods, such as LSTM and ensemble models, which demonstrate higher accuracy and scalability when compared to traditional techniques. This review also compares threat intelligence platforms like AlienVault, CrowdStrike, and Recorded Future, to assess their contributions to dynamic detection frameworks. Finally, the study explores research gaps and proposes future directions for developing scalable real-time detection systems integrating multi-source threat feeds
Efficient Corrosion Detection on Metal Surface Using Deep Learning Technique Ky Phuc, Phan Nguyen; Tin, Tran Lam Trung; Luu, Trong Hieu
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.v13i3.6420

Abstract

This study examines how deep learning models can improve corrosion detection, comparing YOLOv7 with its more advanced version, YOLOv8. Both models were trained on a diverse set of images showing different types and levels of corrosion on metal surfaces. Their performance was assessed using standard industry metrics, including accuracy, F1-score, recall, and mean average precision (mAP). The results clearly show that YOLOv8 outperforms YOLOv7 in all areas. It achieves higher recall, precision, and F1-score, demonstrating its improved ability to detect and classify corroded areas. Notably, YOLOv8 is better at identifying small or early-stage corrosion, which is crucial for timely maintenance. Additionally, it processes images faster than YOLOv7, making it more suitable for real-time applications. This study also suggests integrating YOLOv8 with robotic arms equipped for laser cleaning, allowing for automated and precise corrosion removal. This system could improve maintenance efficiency, reduce costs, and enhance the safety and reliability of infrastructure.
A Comprehensive Survey on Artificial Intelligence – Based Classification of Gastrointestinal & Oesophageal Cancers Benitha Sowmiya, E.; Thaiyalnayaki, S.
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.v13i3.6551

Abstract

The global incidence of Gastrointestinal (GI) disorders has risen dramatically over recent decades, driven chiefly by changes in dietary patterns and lifestyle behaviours; epidemiological evidence attributes nearly two million deaths annually to these conditions, underscoring their substantial burden on healthcare systems. Despite endoscopy’s status as the diagnostic standard for detecting mucosal lesions—such as adenomatous polyps and oesophagitis— its performance is hindered by observer variability, limited reproducibility, and lengthy procedural times. To address these limitations, computer-aided diagnostic (CAD) frameworks have been integrated into clinical workflows, offering enhanced accuracy, throughput, and operational efficiency. AI-based pipelines leveraging advanced Machine Learning (ML) and Deep Learning (DL) architectures have proven highly effective in the early detection of GI malignancies and in quantitatively assessing tumour invasion depth. These technologies not only accelerate critical clinical decisions but also support the development of individualized, precision oncology regimens. This survey provides an in-depth assessment of current ML and ML methodologies applied to GI and oesophageal cancer diagnostics, evaluates established prognostic biomarkers, compares algorithmic performance metrics, and identifies key research directions to overcome existing methodological and translational challenges. Although AI-driven diagnostic systems hold the potential to transform GI oncology by standardizing workflows and improving detection rates, their routine clinical adoption requires rigorous validation in multicentre trials and the establishment of comprehensive implementation guidelines.
Enhancing GRU-Based DRL with Delta-LiDAR for Robust UAV Navigation in Partially Observable Dynamic Environments Haddad, Maryam Allawi; Khudher, Dhayaa Raissan
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.v13i3.7067

Abstract

Partial observability and sensor limitations are challenging for the navigation of autonomous Unmanned Aerial Vehicles (UAVs). Deep Reinforcement Learning (DRL) algorithms have emerged as potential tools in advancing this field. However, their effectiveness degrades in challenging environments, particularly in the presence of dynamic obstacles. Recent research trends emphasize the need for new DRL variants that guarantee robustness, real-time adaptability, and improved generalization under uncertainty. This paper proposes a lightweight DRL architecture that combines Proximal Policy Optimization (PPO) with a Gated Recurrent Unit (GRU), extended with a temporal LiDAR differencing feature called Delta-LiDAR. The difference between consecutive LiDAR scans is computed to provide the velocity and directional cues without the computational burden of Long Short-Term Memory (LSTM) networks. We evaluate three models, PPO-LSTM, PPO-GRU, and Delta-LiDAR augmented PPO-GRU in a 3D simulated UAV navigation environment characterized by noise, clutter, and dynamic obstacles. We considered several metrics, including success rate, collision frequency, trajectory smoothness, and computational efficiency, to determine the effectiveness of each architecture. The experimental results demonstrate that Delta-LiDAR improves GRU-based temporal reasoning. The deployment complexity is reduced compared with the LSTM-based architecture, which makes it ideal for real-time UAV operation in partially observable environments.

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