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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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A Compact Violin-Shaped Monopole Antenna for Ultra-Wideband Applications Alwan, Younes S.; Zidan, Mohammad S.; Ibrahim, Omar J.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5404

Abstract

A case study of a miniature monopole planar fiddle or violin-shaped antenna that can be used in ultra-wideband (UWB) application is carried out. Such violin-shaped antenna is a circular patch accompanied with two circular cuts and overlapped with an elliptical patch on the top of it. It is small in size, simple in structure, feasible to construct and experimentally feasible to be manufactured and validated in lab. Furthermore, the handle of the fiddlelike structure serves as a microstrip 50? feeding line connected to the main patch structure body. However, the prototype of the designed antenna is manufactured on a substrate of a dielectric material of FR4 with a dielectric constant that equals 4.3 with dimensions of 28×18×1.6 mm3. The gain at the resonant frequencies reached different values throughout the covered frequency band; that is of (3.1 GHz up to 13.5 GHz) ranging between the values of (≈1.1 dBi up to ≈5.5 dBi) according to the return loss of the performance outcome. The empirically measured and simulated results have a suitable settlement and/or agreement and computations display that the antenna has a respectable frequency band, radiation, and characteristics of time domain in spite of the antenna’s small size and simple design.
Advanced Techniques for Improved Bangladeshi Number Plate Detection and Character Recognition in Automated Parking Systems Khan, Niaz Ashraf; Bin Hafiz, Md. Ferdous
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5477

Abstract

This paper presents a novel technique for efficient extraction of vehicle number plates from camera-captured images and accurate recognition of Bangla characters embedded within them. With the exponential growth of vehicular traffic in densely populated regions like Bangladesh, automation becomes crucial, making vehicle plate recognition pivotal for tracking stolen vehicles and enhancing traffic control measures. Leveraging conventional computer vision and image processing techniques, our proposed system incorporates specific features inherent to Bangladeshi number plates, thus enhancing recognition accuracy. Our application makes use of the OpenCV library to underscore the strength of the algorithm, which has been confirmed through real-time testing across different weather conditions and varying image qualities. The results show a remarkable accuracy rate of 92.3%, affirming our technique's reliability in vehicle number plate detection and character recognition. Moreover, the integration with MySQL database and Arduino UNO enables real-time application in automated parking systems, offering seamless entry procedures and accurate billing, thus addressing critical concerns in modern transportation management systems. Our algorithm not only enhances security measures but also streamlines parking facility management, contributing to safer and more efficient urban mobility solutions.
Semantic Similarity Measure Using a Combination of Word2Vec and WordNet Models Fellah, Aissa; Zahaf, Ahmed; Elçi, Atilla
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5114

Abstract

The cognitive effort required for humans to perceive similarities and relationships between words is considerable. Measuring similarity and relatedness between text components such as words, texts, or documents is challenging, and it continues to be an active area of research across various domains. The complexity of language and the diverse factors that influence similarity and relatedness make this task an ongoing research focus. Researchers are exploring diverse approaches, to improve the accuracy and effectiveness of measuring similarity and relatedness in text. The utilization of knowledge sources, such as WordNet, has been a popular approach for modeling semantic relationships between words. However, Recently, distributional semantic models, such as Word2Vec, have demonstrated their ability to effectively capture semantic information and outperform lexiconbased methods in terms of unidirectional contextual similarity outcomes. In contrast to lexicon-based approaches, which rely on structure, distributional models leverage context to capture semantics. This study proposes a novel approach that linearly combines the lexical databases WordNet and Word2Vec to measure semantic similarity, focusing on improving upon previous techniques. The proposed approach is thoroughly detailed and evaluated using popular datasets to determine its effectiveness. The experimental results indicate that the proposed approach achieves highly satisfactory results and surpasses the performance of individual methods.
Cybersecurity Implications of 5G Networks: Threats, Potential Vulnerabilities, and Their Implications for National Security and Privacy Egho-Promise, Ehigiator; Asante, George; Balisane, Hewa; Salih, Abdulrahman; Aina, Folayo; Kure, Halima
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6573

Abstract

The rapid expansion of Fifth Generation (5G) networks represents a revolutionary shift in telecommunications technology, offering increased speed, higher connection density, and enhanced network efficiency. Nevertheless, these benefits have also attracted various security risks that threaten the protection of national security and the privacy of private citizens. This research investigates the cybersecurity challenges associated with 5G networks by analysing emerging threats, assessing vulnerabilities in 5G infrastructure, and evaluating their impact on national security and individual privacy. The research approach includes a literature review of various sources of knowledge and regulations or policies, as well as a quantitative analysis of network vulnerabilities through penetration testing and threat modelling. The study's results indicate that network slicing introduces new risks to a network, as it provides potential attackers with easy access to weaknesses that exist within isolated network slices. Furthermore, the incorporation of Internet of Things (IoT) devices increases the overall risk, as they often lack proper security measures. Lastly, the multi-tenant characteristic of 5G networks poses a challenge in creating secure isolation between various operators and service providers. This makes it imperative for organisations and service providers to enhance their security measures, such as encryption and access control policies, as well as overall policies, to help rectify these issues. These findings concluded significant implications across national security and privacy fronts. The study re-emphasizes the importance of a multi-sectoral approach to cybersecurity by industries, policy-makers, and academic scholars. Measures and techniques that are relevant to implementing specific safety tactics and regulations were proposed. The results of this study serve as a reference for 5G cybersecurity. The results offer recommendations that are useful in developing security measures to counter threats and improve the security posture of future 5G networks.
A Message Transmission Scheme for IoT Inspired by The Shamir Scheme and Based on The Hidden Number Problem (HNP) El Ouafi, Mohamed; Aslimani, Abderrahim; Lamrini Uahabi, Kaoutar; Zannou, Abderrahim
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7366

Abstract

The Internet of Things (IoT) requires cryptographic mechanisms that are both lightweight and resistant to emerging attacks. Classical public-key protocols such as RSA or ElGamal, as well as many post-quantum lattice-based schemes, consume too many resources for devices with limited memory, processing power, and energy. In this study, we propose a three-pass message transmission protocol that avoids any prior key exchange. Inspired by Shamir’s keyless scheme and relying on the hardness of the Hidden Number Problem (HNP) and the Decisional Diffie–Hellman (DDH) assumption, the protocol operates in a finite field with safe primes and refreshes random masks at each execution, providing strong resistance to brute-force and small-subgroup attacks. We formally prove IND-CPA security and implement the HNP 3-pass scheme, showing that each pass executes in 1.4--1.8 ms on a workstation, with 132-byte public keys and 192--256-byte secret keys. Estimated energy consumption per iteration is 101.562 mJ. Comparative simulations on a workstation and embedded platforms (Arduino Uno and Raspberry Pi) against RSA-512, ECC (secp256r1/secp521r1), and post-quantum Kyber-512 show that our scheme achieves execution times comparable to ECC and Shamir’s 3-pass protocol, is significantly faster than RSA, and consumes less energy than Kyber-512. This combination of low latency, moderate key sizes, and energy efficiency highlights the practicality of the HNP 3-pass protocol for resource-constrained IoT environments
Deep Learning for Arabic Question Classification: Leveraging BERT and Hybrid Neural Networks Khedimi, Somia; Bouziane, Abdelghani; Bouchiha, Djelloul
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6312

Abstract

This paper presents a deep learning approach for Arabic question classification, leveraging the strengths of pre-trained language models and advanced neural network architectures to address the unique challenges of Arabic text processing. The proposed methodology employs BERT and Word2Vec to generate contextualized and semantic-rich representations of Arabic questions, effectively capturing their linguistic intricacies and morphological complexity. These embeddings are fed into a hybrid classification framework combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enabling the extraction of both spatial and sequential features from the input. Experimental results demonstrate the model’s effectiveness, achieving an accuracy of 85.12%, along with high precision, recall, and F1-score metrics. These findings highlight the potential of integrating pre-trained Arabic-specific language models with hybrid deep learning architectures, providing a robust solution for Arabic question classification. This work contributes to advancing Arabic natural language processing, offering a strong foundation for the development of high-performance question-answering systems and related applications.
Enhancing Recommendation Systems Through a Hybrid FuzzySparse Similarity Model and Optimizing Noise Reduction Attar, Kausar Salim; Jadhav, Ashish
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6780

Abstract

The vast amount of information available on the Internet and e-commerce has led to the development of recommendation systems that help users find relevant products or content. As digital applications continue to grow worldwide, ensuring the right user experience in a short period of time remains a significant challenge. With the increasing use of mobile devices, ensuring access to accurate and timely information has become an essential part of today's business operations. The accuracy of the estimates depends not only on the methodology used but also on the accuracy of the data. External factors and unexpected noise issues can affect users in the rating process. This problematic influence, coming from well-intentioned users, can lead to distortion of the rating results during the recommendation process. In this study, we present a Hybrid Fuzzy-Sparse Similarity (HFSS) methodology designed to improve the accuracy of recommendations and reduce the error caused by source noise due to rating sparsity. Initially, a limited amount of data utilized in a form of rating matrix along with the sparse distribution is collected for the analysis of the recommendation process. An extended fuzzy set matrix creation mechanism is proposed to solve the existing sparsity problems. By using the extended sparse matrix, the similarity of models is calculated from the complex set theory, which allows for model-based recommendations. The proposed HFSS model is evaluated on the MovieLensdatabase. First, the system recommendation performance evaluation is made, and later a comparison performance is measured by MAE, RMSE, and F1 score metrics, which demonstrates better recommendation accuracy and performance than the comparing performance-based methods.
Hybrid Stacking Ensemble Model for Breast Cancer Classification: Performance Optimization with Existing Machine Learning Models Khairnar, Prerana Nilesh; Venkatesh, R.; Anjum, Asma; P S, Dinesh; Narayanaswamy, Prabakaran; Rajendran, Beaulah Jeyavathana
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7200

Abstract

Prompt and proper recognition of breast cancer Classification (BCC) is imperative in resolution of patient results. In this paper, we suggest a new hybrid Hybrid Stacking Ensemble Model (HSEM), which combines Random Forest, Support Vector Machine and XGBoost classifiers as base learners, and logistic regression as the meta-learner. The HSEM is meant to take advantage of the complement of tree-based and kernel-based algorithms by deriving robust and generalizable binary classification of breast cancer through the use of the Wisconsin Diagnostic Breast Cancer dataset. The accuracy, ROC AUC, and feature importance analysis are considered key metrics that are rigorously tested and compared with traditional standalone models in terms of performance. Findings indicate that the HSEM performs better than traditional classifiers: its accuracy is 99%, and its AUC is 1.00, which makes the method even more viable and reliable when it comes to its prediction values. Learning curves and comparisons further confirm the efficiency of the given approach to be visualized. These results emphasize the possibility of using the Hybrid Stacking Ensemble Model as an efficient instrument of use in medical diagnosis purposes, with the subsequent benefits of providing medical professionals with better diagnostic work support options. The suggested hybrid stacking ensemble not only compares, but also improves performance by combining tree-based and kernel-based learners. The comparative evaluation shows that the optimized hybrid technique always works better than current single-model and ensemble-based methods.
An Adjacent Gray Code Pairing Approach for Fault Identification in Reversible Circuits Kalita, Dimpimoni; Handique, Mousum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7097

Abstract

The classical computing works on the concept of irreversibility increasing the heat dissipation in computing machinery. The remarkable ability of reversible computing is to reduce this dissipation of heat and to generate lossless information. The reversible circuit is the way to implement the reversible function. Therefore, the perfection of the functional behavior of the circuit plays a vital part within the realm of testing. Occurrence of faults in the reversible circuits creates a dysfunctional behavior in the circuit. Here in this study, a fault detection approach has been devised for reversible circuits that effectively identifies all categories of Missing Gate Faults (MGFs), such as single, multiple, partial, and repeated gate faults, through the use of the Adjacent Gray Code Pairing (AGCP) technique. The approach includes a process of conversion of binary to gray codes and pairing of non duplicative consecutive adjacent codes by which test vectors can be achieved for finding the respective faults with a 50\% reduction in test set size. An experimental study and comparative analysis with existing methods have been carried out using a variety of standard reversible circuits.
Rasa-Powered Conversational AI Framework for Intelligent Electric Vehicle Trip Planning and Energy Management T, Phanendra; G, Swapna; M, Vishnuvardhan; Rao, K V Govardhan; Rakesh, T; Kumar, M Kiran
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6553

Abstract

The rapid growth of Electric Vehicles (EVs) calls for intelligent solutions to optimize grid stability and enhance user experience. This paper proposes a geo-optimized, user-centric EV management system integrating open-source geospatial tools with Rasa-based Natural Language Understanding (NLU). Through an interactive conversational interface, EV owners receive real-time trip planning recommendations based on parameters such as State of Charge (SoC), charging point availability, and route efficiency. The system utilizes APIs for real-time geolocation, charging station data, and Battery Management System (BMS) insights to determine optimal charging locations, durations, and trip costs. Extensive testing demonstrates improved energy management and route planning efficiency, highlighting the system’s potential for smart and sustainable EV infrastructure development.

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