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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 25 Documents
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Search results for , issue "Vol 12, No 4: December 2024" : 25 Documents clear
Photometric Stereo-based Woven Fabric Pattern Recognition Using Wavelet Image Scattering Setiawan, Irwan; Juliastuti, Endang; Kurniadi, Deddy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5589

Abstract

The weave pattern is a crucial factor that enhances the strength and stability of the fabric. Pattern recognition of woven fabric based on vision methods has been widely developed. In this research, woven fabric's basic weaving pattern recognition is based on photometric stereo images. First, six images of woven fabric were taken, each with a different direction of light. Next, an unbiased photometric stereo algorithm was used to reconstruct the six images. This paper used 23 grayscale photometric stereo images measuring 400 x 300 pixels. Augmentation techniques were carried out to produce 458 images consisting of 240 plain woven images, 159 twill woven images, and 60 satin woven images. The training data set consists of 367 images, and testing consists of 192 images. The feature extraction method uses wavelet image scattering and classification using Principal Component Analysis (PCA) and Support Vector Machine (SVM). The wavelet image scattering method effectively extracts texture features of photometric stereo images of diverse woven fabrics, while the PCA and SVM methods successfully classify the basic woven fabric patterns. The results of recognizing the basic woven fabric pattern using PCA and SVM classification obtained an accuracy of 98.57%.
Predictive Analysis of Learner’s Performance in Online Environments with LSTM and Attention Mechanism Nanavaty, Smruti; Khunteta, Ajay
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5635

Abstract

Early identification and supporting at-risk learners is a key problem in digital learning environment. This paper investigates the use of deep learning methods, namely Long Short-Term Memory (LSTM) neurons with cognitive mechanisms to determine those learners that are most likely to be at risk based upon the involvement of the learners in periodic assessment as well as engagement with the learning components in online learning environments. It also accounts for the relevance of dependencies of temporal elements, which adds a degree of precision in forecasting. The findings show how advanced analysis of data can potentially improve student support strategies with online learning systems, thus ensuring the success and retention of learners in consequence. From the test, result yield information concerning the robustness of the LSTM model in predicting the learner's achievement and provides insight into factors that most importantly have an impact on prediction. That suggests the approach of LSTM with attention mechanism is effective to capture periodic behavior of the learner on virtual platforms and early predictions will be useful for administrators to design timely intervention and improve retention rates of learners.
Assessing MANET Routing Protocols: Comparative Analysis of Proactive and Reactive Approaches with NS3 Ahmed, Khandaker Takdir; Godder, Tapan Kumar; Al Mahmud, Tarek Hasan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5761

Abstract

MANETs are dynamic, decentralized networks that employ mobile nodes as hosts and routers. High mobility and frequent topology modifications make routing triky in these networks. This research compares the efficacy of four MANET protocols: DSR, AODV, OLSR, and DSDV. We evaluate various protocols utilizing the NS3 simulator for PDR, throughput, control overhead, and delay. We analyze each protocols strong points and weaknesses under varied node densities, pause durations, and network sizes. DSR has the greatest PDR and lowest control overhead, making it ideal for dynamic networks. OLSR maintains high throughput and short delay despite increasing control overhead. DSDV has the maximum throughput but significant control overhead and PDR in bigger networks. AODV performs well in smaller networks but degrades significantly as network size rises. This research illuminates MANET routing protocol trade-offs, helping to build more resilient and efficient communication techniques for diverse application situations. Our results imply that DSR is best for dynamic contexts and OLSR for route availability and low latency.
An Approach for Improving Accuracy and Optimizing Resource Usage for Violence Detection in Surveillance Cameras in IoT systems Vo, Hoang-Tu; Tien, Phuc Pham; Thien, Nhon Nguyen; Mui, Kheo Chau
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5787

Abstract

Smart farming that uses information and communication technology is developed as a critical technique to address the challenges related to agricultural production, environmental effects (climate change), food security, and supply chain. The recent statistics reveal that the world's population has been increased significantly, which is expected to reach 7.7 billion. It is essential to achieve a significant rise in food output to meet the requirement of such a massive growth of population. However, due to the natural conditions and a variety of plant illnesses, food productivity and farms are reduced. In order to diagnose food diseases in farming, new technologies like the Internet of Things and artificial intelligence are now essential. To this end, the research paper introduces a novel artificial intelligence model represented by a twelvelayer deep convolution neural network to identify and classify plant image diseases. 38 distinct types of plant leaf photos are used for training and testing the suggested model, which are obtained by adjusting different parameters such as (a) hyperparameters; (b) coevolutionary layers; (c) and pooling layers in number. The proposed model consists of an extractor and classifier of functions. The first section involves three phases, i.e., it consists of two convolution layers and a maximum pooling layer for each phase. The second section consists of three levels: flattening, hidden, and output layers. The proposed model is compared with LeNet, VGG16, AlexNet, and Inception v3, which are considered state-of-the-art pre-trained models. The results demonstrate that the accuracy of LeNet, VGG16, AlexNet, and Inception v3 is given as 89%, 93%, 96.11%, and 97.6%, respectively. The findings provided in this research show that the suggested model outperforms state-of-the-art models in terms of training speed and computing time. Also, the results show that the proposed model achieves a considerable improvement in terms of accuracy and the mean square error compared to the state-of-the-art methods. In particular, The outcomes demonstrate that the suggested model achieves a mean square error and prediction accuracy of 98.76% and 0.0580, respectively. The results also depict that the proposed model is more reliable, allows fast convergence time in obtaining the results, and requires only a small number of trained parameters to identify the plant diseases accurately.
A Twelve-layer Deep Convolution Neural Network for Fast, Efficient and Reliable Identification and Classification of Plant Diseases in Smart Farming Jassim, Hafsa A.; Taha, Zahraa Khduair; Nawar, Abbas Khalifa
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5455

Abstract

Smart farming that uses information and communication technology is developed as a critical technique to address the challenges related to agricultural production, environmental effects (climate change), food security, and supply chain. The recent statistics reveal that the world's population has been increased significantly, which is expected to reach 7.7 billion. It is essential to achieve a significant rise in food output to meet the requirement of such a massive growth of population. However, due to the natural conditions and a variety of plant illnesses, food productivity and farms are reduced. In order to diagnose food diseases in farming, new technologies like the Internet of Things and artificial intelligence are now essential. To this end, the research paper introduces a novel artificial intelligence model represented by a twelve-layer deep convolution neural network to identify and classify plant image diseases. 38 distinct types of plant leaf photos are used for training and testing the suggested model, which are obtained by adjusting different parameters such as (a) hyperparameters; (b) coevolutionary layers; (c) and pooling layers in number. The proposed model consists of an extractor and classifier of functions. The first section involves three phases, i.e., it consists of two convolution layers and a maximum pooling layer for each phase. The second section consists of three levels: flattening, hidden, and output layers. The proposed model is compared with LeNet, VGG16, AlexNet, and Inception v3, which are considered state-of-the-art pre-trained models. The results demonstrate that the accuracy of LeNet, VGG16, AlexNet, and Inception v3 is given as 89%, 93%, 96.11%, and 97.6%, respectively. The findings provided in this research show that the suggested model outperforms state-of-the-art models in terms of training speed and computing time. Also, the results show that the proposed model achieves a considerable improvement in terms of accuracy and the mean square error compared to the state-of-the-art methods. In particular, The outcomes demonstrate that the suggested model achieves a mean square error and prediction accuracy of 98.76% and 0.0580, respectively. The results also depict that the proposed model is more reliable, allows fast convergence time in obtaining the results, and requires only a small number of trained parameters to identify the plant diseases accurately.
Deep Learning Techniques for Advanced Drone Detection Systems: A Comprehensive Review of Techniques, Challenges and Future Directions Muhamad Zamri, Fatin Najihah; Gunawan, Teddy Surya; Kartiwi, Mira; Pratondo, Agus; Yusoff, Siti Hajar; Mohd. Mustafah, Yasir
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.6028

Abstract

The widespread use of Unmanned Aerial Vehicles (UAVs), commonly known as drones, across various sectors, such as civilian, commercial, and military operations, has created significant challenges in ensuring security, safety, and privacy. This paper provides a comprehensive review of the latest advancements in drone detection systems leveraging deep learning techniques, covering the period from 2020 to 2024. It critically evaluates both optical (visible light and thermal infrared) and non-optical (radio frequency, radar, and acoustic) detection methodologies. The analysis includes cutting-edge models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), focusing on their application in drone detection. Key challenges like real-time processing, environmental interference, and differentiation between drones and similar objects are examined. Potential solutions, including sensor fusion, attention mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT) and 5G networks, are discussed in detail. The paper concludes with future research directions to enhance drone detection systems' robustness, scalability, and accuracy, particularly in complex and dynamic environments. This review offers valuable insights for researchers and industry professionals working towards next-generation drone detection technologies.
Artificial Intelligence-Based DS-PSO Algorithm for Enhanced Frequency Response in Digital IIR Filters Abdulhussien, Wijdan Rashid; Al-Safi, Jehan Kadhim Shareef; Jwaid, Wasan M.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5885

Abstract

Digital elliptic filters, as a type of infinite impulse response (IIR) digital filter, play a crucial role in signal processing applications. Despite their widespread use, there remains a significant research gap in optimizing their frequency response to better approximate desired magnitude responses. This study addresses this gap by introducing an innovative optimization technique that leverages the DS-PSO (Dynamic & Static-Particle Swarm Optimization) algorithm. Based on artificial intelligence, the DS-PSO method uniquely integrates topologies (dynamic and static) into particle swarm optimization (PSO), enabling more precise analysis of pole positions derived from a filter's transfer function coefficients. The primary research problem lies in approximating the frequency response of digital IIR elliptic filters to match a desired magnitude response. Traditional methods often fail to achieve this due to limitations in their optimization techniques. The proposed DS-PSO algorithm addresses this by setting a slightly more significant maximum pole radius (Rmax) than 1.0, surpassing the pre-established pole radius (R). This approach allows for a more accurate approximation of the frequency response. This feature distinguishes it from previous studies that employed genetic algorithms (GA) and semi-definite programming (SDP) techniques, which reported lower Rmax values. The results of this study demonstrate the effectiveness of the DS-PSO algorithm in improving the frequency response of digital IIR elliptic filters. The proposed method successfully approximates the desired magnitude response by designing 4th and 12th-order lowpass digital IIR elliptic filters while maintaining stability at a high average. This makes the technique particularly suitable for determining frequency response boundaries in electronics or communications systems. The contribution of this research extends beyond the immediate results. By introducing and validating the DS-PSO algorithm, this study provides a robust framework for future research in optimizing digital IIR filters. The findings not only enhance the design of digital elliptic filters but also open new avenues for improving other types of IIR filters and signal processing applications. This paper establishes a foundation for further research in signal processing and other fields, with significant theoretical and practical implications.
Webcam Based Robust and Affordable Optical Mark Recognition System for Teachers Somaiya, Effat; Sun Mim, Alifa; Abdul Kader, Mohammed
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5856

Abstract

The growing need for efficient automated grading solutions has driven advancements in optical mark recognition (OMR) systems for multiple-choice assessments. This paper introduces a novel webcam-based OMR system that employs advanced image processing and computer vision techniques to eliminate the dependency on specialized hardware. The proposed system enhances image quality, extracts relevant data, and accurately processes marked responses through a robust pipeline of preprocessing, segmentation, and recognition algorithms. Addressing challenges such as inconsistent handwriting styles and varying lighting conditions, the system demonstrates high accuracy and reliability, achieving an impressive accuracy rate of 100%. Experimental validation highlights significant improvements in grading efficiency, reduced human error, and enhanced consistency when compared to manual grading methods. The scalability of the system makes it applicable to remote learning environments, online exams, and large-scale assessment scenarios. Future research directions include integrating machine learning techniques to extend the system’s capabilities to subjective assessments and potential collaborations with educational institutions and online platforms. This research contributes to the field by providing an accessible and scalable automated grading solution that optimizes assessment workflows and improves the educational experience.
Passive Intermodulation Cancellation in 5G Systems Using Artificial Neural Networks Gharaibeh, Khaled M
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5899

Abstract

Passive intermodulation (PIM) has been a serious challenge in 5G Frequency Division Duplexing (FDD) carrier aggregated (CA) wireless systems leading to the degradation of system performance. Digital cancellation techniques have been used to dynamically cancel the time-varying PIM resulting from passive nonlinearities. These techniques are usually based on Volterra-like behavioral models which are very complex and hard to implement. In this paper, a Feedforward Neural Network (FFNN)-based PIM cancellation scheme is proposed for PIM cancellation in a CA FDD wireless system. Simulation of the proposed scheme shows that the FFNN cancellation scheme is capable of acheving above 20-dB PIM cancellation ratio over a 30-dB input power range.
Joint encryption and error correction schemes: A survey Chothe, R. V.; Ugale, S. P.; Chandwadkar, D. M.; Shelke, S. V.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5799

Abstract

In this recent era, the sharing of critical information is essential. Along with information security, error-free data transmission is equally important. Crypto-coding is the method combining encryption algorithms and error correcting codes to enhance performance in terms of security, time, resources, or complexity. Despite the significant research, a comprehensive systematic literature survey that explores the status of research is not available. The proposed study fills this gap by exploring the available research in detail and highlighting past contributions, allowing readers and upcoming researchers to have a detailed understanding of various software and hardware implementations of crypto-coding algorithms and their results. This paper presents a comparison of encryption methods based on various parameters. The crypto-coding research work in satellite communication is also added. The survey includes implementation methods, results, applications, and comparisons of previous work results. This systematic literature survey also covers open issues and future trends of solutions in this context. The paper will offer directions for potential research in the area of crypto-coding and will expand the research frame for upcoming scholarly research.

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