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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 17 Documents
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Search results for , issue "Vol 13, No 2: June 2025" : 17 Documents clear
Trainer Kit for Aroma Classification Using Artificial Intelligence Istiyanto, Jazi Eko; Lelono, Danang; Natan, Oskar; Khamila, Shafa; Adhiyant, Hafizha; Abda’i, Ikhlasul Amal; Adzaqi, Ilyaz Raukhillah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6146

Abstract

This research focused on the development and evaluation of machine learning algorithms for aroma classification using sensor data, implemented within the e-Trainose system. Various algorithms, including Neural Network, Support Vector Machines, and Random Forest, were tested to determine their effectiveness in distinguishing between different aroma samples, namely alcohol, coffee, and tea. The study utilized an array of metal oxide semiconductor sensors to capture the volatile organic compounds associated with each aroma. The features tested included sensor responses such as resistance changes and Gaussian smoothing of sensor data. Among the algorithms tested, Neural Network demonstrated the highest accuracy (98.89%), precision (99.10%), recall (99.10%), and F1 score (99.10%), making it the most reliable model for this task. These results highlight the potential of using machine learning with e-Trainose for real-time aroma detection and classification. The research paves the way for future advancements in the field, including the development of hybrid models and further optimization of sensor-based classification systems.
A New Fault Tolerant Scheme for Switch Failures in LLC Resonant Converter Lili, Xu; Hidayat, Muhamad Nabil; Nik Ali, Nik Hakimi; Umair, Muhamad
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6288

Abstract

The LLC (Inductance Inductance capacitance) resonant converter offers advantages such as high power density, high efficiency, and compact size, making it widely used in photovoltaic power generation systems. Its operational reliability is crucial for the continuous performance of these systems. However, complex operating conditions and variable climates can adversely affect power equipment. Switch fault diagnosis and remedial measures are essential aspects of designing isolated full-bridge DC-DC converters, significantly enhancing overall system reliability. When a switching component fails, the resonant converter cannot operate near its resonant point, leading to substantial reductions in efficiency and output power. To improve system fault tolerance and reduce maintenance costs, this paper proposes an improved LLC topology and a rapid switch short-circuit fault diagnosis method for phase-shift full-bridge converters. By real-time monitoring of the average voltage of the resonant capacitor, the method quickly identifies switch short-circuit faults within a single switching cycle, enabling topological control of faulty and redundant components. The modified topology ensures stable output voltage and power while allowing the converter to operate near the resonant frequency. The paper discusses the working principle, design considerations, and implementation of this approach. Simulation results verify the effectiveness of the proposed method.
Fractional Order Sliding Mode Control to Mitigate Power Quality Issues using Dynamic Voltage Restorer in Distribution Network Yaqub, Muhammad Haroon; Hanif, Aamir; Anwar, Naveed; Ullah, Mian Farhan; Shah, Muhammad Shahzaib; Hussein, Aziza I.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6031

Abstract

Power quality (PQ) issues lead industrial customers to suffer significant financial losses. These PQ issues are garnering more attention from electricity suppliers and consumers in the modern day. This study addresses prevalent PQ issues, namely voltage sag and swell, stemming from a decrease in RMS voltage within electrical networks, particularly impacting sensitive loads. The solution proposed involves employing a series connected custom power device (CPD) named as dynamic voltage restorer (DVR) with an integrated DC battery for energy storage, to consistently maintain the requisite voltage magnitude. To effectively combat voltage sag and swell, the study introduces a novel control strategy known as fractional order sliding mode control (FOSMC). Noteworthy features of the FOSMC methodology include its capacity to autonomously and dynamically address sag and swell issues. The Simscape toolbox of MATLAB®/Simulink® is used to perform simulations to showcase the efficacy of the FOSMC technique. The results demonstrate that this strategy ensures total harmonic distortion remains below 5% and achieves sag/swell mitigation in less than 2 milliseconds, aligning with SEMI-F-47 and IEEE voltage standard 1159-2019. In summary, the study introduces and validates a robust control strategy implemented in a DVR system to autonomously alleviate voltage sag and swell issues, with simulation results supporting its effectiveness in upholding PQ standards. The FOSMC scheme with DVR is also compared with FOSMC scheme with DSTATCOM as well as with super twisting sliding mode control (STSMC) algorithm and classical sliding mode controller (SMC) to show the effectiveness of the proposed scheme. The FOSMC technique with DVR is more effective in restoring voltage sag/swell and PQ issues.
Efficient Strategies for a Medium Voltage Loop Powered by an Infinite Source Mahmoud, Ethmane Isselem Arbih; Abbou, Ahmed; Mahmoud, Abdel Kader; Eida, Né Dah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6114

Abstract

This paper analyzes and examines the potential of an infinite generation system to support the domestic load growth of the 33 kV loop network from 2025 to the year 2040. The study assesses the current state of the network, focusing on voltage levels, line loadings, and transformer capacities to ensure that all components operate within the system's allowable loading limits. It is assumed that the loop is powered by an infinite source. A numerical model, utilizing the Gauss-Seidel method, is developed and run using the PSS/E simulator and ETAP. The voltage profile is expected to remain within the range of 0.95 to 1.05 pu. An analysis of the simulation results demonstrates the potential for increasing active power transfer and controlling reactive power in the system by the year 2040.Furthermore, solutions are proposed to address identified critical issues in order to meet the projected demand. These include doubling the capacity of existing transformers and implementing protection against short-circuit currents. The proposed system is expected to provide industrial consumers with reduced load imbalances and improved control over voltage fluctuations caused by rapid changes in reactive power demand.
Deep Learning-Driven Intrusion Detection System for Distributed Denial of Service Mitigation ben Rhouma, Wala; Hayouni, Haythem
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6013

Abstract

DDoS attacks continue to pose a serious risk to digital infrastructures, as they can render online services inaccessible without altering system files or gaining direct control over the target. Traditional security mechanisms often fall short in identifying these attacks promptly due to their massive scale and the subtlety with which they blend into regular traffic. With the advancement of artificial intelligence, especially in the realm of deep learning, new solutions are emerging to enhance the detection and classification of such threats. In this work, we focus on strengthening Intrusion Detection Systems (IDS) by leveraging deep learning methods to improve accuracy and responsiveness in detecting DDoS attacks. Using the comprehensive CIC-DDoS-2019 dataset, we experimented with several deep learning architectures including Feedforward Neural Networks (MLP), Convolutional Neural Networks (CNN), and Recurrent models incorporating Long Short-Term Memory (LSTM). These models were evaluated for their ability to analyze complex traffic behaviors and identify malicious activity within diverse network environments. his study contributes to the ongoing research on intelligent cybersecurity solutions by proposing deep learning-based IDS frameworks that not only detect threats with higher accuracy but also adapt to dynamic attack patterns. Our findings suggest that such models can serve as a critical component in modern security infrastructures, offering scalable and resilient defense mechanisms against increasingly sophisticated cyberattacks like DDoS. Our empirical results demonstrate that the MLP model yielded the most reliable performance, achieving an outstanding classification precision of 99.62% across various traffic categories. This highlights its effectiveness in isolating harmful flows from legitimate ones, thereby reducing the risk of false alarms and improving detection reliability.
Closed-Form Solution for Energy Efficiency Maximization in Uplink IRS-Assisted Multi-User NOMA Network Meftah, El-Hadi; Benmahmoud, Slimane
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6209

Abstract

Given the growing concerns about energy consumption and its negative impact on the ecosystem, energy efficiency (EE) has become one of the most important key performance indicators in current and future wireless communication tech nologies. In this paper, we address the EE maximization problem in an uplink intelligent reflective surface (IRS)-assisted multi-user non-orthogonal multiple access (NOMA) network. This problem is formulated as a trade-off between the spectral efficiency (SE) and total power consumption, and it appears to be non convex. To avoid the complexity associated with the traditional iteration-based Dinkelbach method, we opt for an alternative closed-form solution for the users’ transmit power based on partial derivative analysis and Lambert function. Simulation results with a realistic power consumption models confirm the accuracy of our theoretical findings.
Mitigating Wormhole Attacks’ Risks within Wearable Body Network Goumidi, Mohammed Abdessamad; Zigh, Ehlem; Ali-Pacha, Adda Belkacem
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6216

Abstract

In this research, we sought to develop a trust and secure routing protocol based on the Ad-hoc On-Demand Distance Vector (AODV) routing to combat wormhole attacks in Wearable Body Networks (WBNs), which integrates a routing strategy that leverages the path-checking method to detect and isolate paths affected by wormhole attacks effectively, it employs a routing technique that prioritizes nodes with the most heightened remaining energy during data transmission, along with a mixed cryptographic algorithm that combines the modified One Pad Time with the modified Affine ciphers to ensure safe transmission against malicious biosensor threats. Experimental findings indicate that our proposed protocol transcends the classic AODV routing protocol across all evaluation parameters, including packet delivery ratio, throughput, and energy consumption. Its primary advantage lies in considering multiple factors, like detecting unauthorized biomedical biosensors, efficient energy utilization in the network, and secure data transmission—differentiating it from other safe routing protocols. Moreover, the mixed encryption algorithm enhances efficacy and bolsters sensitive data security compared to classic cipher methods like the One Pad Time and Affine ciphers.
Advanced Classification of Agricultural Plant Insects Using Deep Learning and Explainability Vo, Hoang-Tu; Thien, Nhon Nguyen; Mui, Kheo Chau; Tien, Phuc Pham; Le, Huan Lam; Phuc, Vuong Nguyen; Trung, Hieu Nguyen; Tan, Phuong Lam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6419

Abstract

This paper investigates the effectiveness of six pre-trained deep learning models to classify images of agricultural plant insects. We utilized the BAUInsectv2 dataset, which includes images from nine classes. Aphids, Armyworm, Beetle, Bollworm, Grasshopper, Mites, Mosquito, Sawfly, and Stem borer. The models, namely Xception, MobileNetV2, ResNet50, EfficientNetV2B3, ResNet101, and DenseNet121, are fine-tuned by transfer learning from ImageNet. This approach significantly reduces training time while improving classification accuracy. Our experiments reveal that each model reliably distinguishes between insect species even when faced with varying lighting conditions and diverse viewpoints. To further clarify how these models make predictions, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight critical regions in the images. The results demonstrate that each model focuses on unique biological features and offers clear explanations for its decisions. The research results contribute to demonstrating the potential of pre-trained deep learning architectures for agricultural monitoring and pest management, paving the way for promising future applications.
Benchmarking of OFDM Spectrum Exchange for Mobile Cognitive Radio Networks Marwanto, Arief; Kamilah, Sharifah; Satria, M. Haikal
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.4094

Abstract

The local spectrum sensing objective in spectrum sensing is to detect the PU's signal. The sensing node's (SN) capacity to detect the PU's signal is of paramount importance. However, it is presumed to be stationary in the majority of SN in cognitive radio networks. The detection performance on local observation is significantly influenced by the mobility of the PUs and SNs. The SNs' movement generates spatial diversity in the PU's signal observation. The signal's condition would fluctuate during the sensing process as a result of Doppler effect, spatial distance, velocity, movement, and geolocation information. Therefore, a benchmark is required to compare the primary user signal detection level of stationary and moving SNs from each sensing node. The performance results have demonstrated that static nodes with SCM are superior to conventional subcarrier mapping (SCM) methods in the case of a subcarrier mapping width of α = 2. Additionally, the quantization width is uniform. It has been determined that the performance disparity is substantial, ranging from 2 dB to 4 dB. The results indicate that the static nodes SCM have achieved acceptable performance detection at a low subcarrier detection threshold (SDT) value of 0 dB up to 5 dB. Conversely, the probability of conventional SCM detection is less than 1 of probability detection (PD) value at the same low SDT value. The detection probability (PD) of static nodes with SCM is satisfactory at an SDT value of 15 dB. Moreover, the probability begins to decline until 20 dB at an SDT value of 11.5 dB, a substantial decrease that is rendered negligible. In contrast to the new subcarrier mapping (N-SCM) method, which has a false alarm probability (PFA) of approximately 0 dB to 9.5 dB, conventional subcarrier mapping (SCM) has a high false alarm probability in mobility networks. Furthermore, it is evident that the PFA curves for the conventional SCM method are lower than those of other methods at low speeds, as they approach the null value at SDT 7.5 dB. The PFA curve for both methods is higher than other velocities by attaining a null value at 10 dB, in contrast to high velocity. In general, the mobility parameter has the potential to meet the detection performance and perform well in the false alarm probability of mobile spectrum exchange. Consequently, it could be employed to provide information on spectrum exchange in the future.
BCDNN: Enhancing CNN Model for Automatic Detection of Breast Cancer Using Histopathology Images Anumalla, Koushik; Kumar, G. Sunil  ; Vani, M. Sree; Rao, Kuncham Sreenivasa
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.5854

Abstract

The United Nations has identified health and well-being for all as one of its sustainable development goals. Research efforts in the healthcare domain worldwide are aligned with this goal. According to the World Health Organization (WHO), there has been an increasing incidence of breast cancer globally. The emergence of Artificial Intelligence (AI) has enabled learning-based approaches for diagnosing various ailments in the healthcare domain. Numerous efforts have been designed to efficiently diagnose breast cancer using deep learning algorithms, with the Convolutional Neural Network (CNN) being the widely used model due to its efficiency in processing medical images. However, CNN-based models may experience deteriorated performance without empirical studies to improve the underlying architecture. Motivated by this fact, our paper proposes a deep learning-based system for breast cancer diagnostic automation by enhancing a CNN model called the Breast Cancer Detection Neural Network (BCDNN). We also introduce an algorithm called Enhanced Deep Learning for Breast Cancer Detection (EDL-BCD), which leverages the enhanced deep learning model for better disease diagnosis performance. Our evaluation with a benchmark dataset comprising breast histopathology images shows that our suggested framework significantly outperforms state-of-the-art models, achieving an impressive accuracy of 97.99%. Therefore, the proposed system can be integrated with healthcare applications to assist in automatic screening by utilizing histopathology pictures to visualize breast cancer.

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