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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 22 Documents
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Search results for , issue "Vol 12, No 3: September 2024" : 22 Documents clear
Synthesis of Bandpass Filter as a Four-Pole Based on a Non-Homogeneous Line Kozlovskiy, Valeriy; Kozlovskiy, Valerii; Boiko, Juliy; Balanyuk, Yuriy; Yakymchuk, Natalia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5537

Abstract

The article deals with the synthesis of band-pass filters (BPF) for the design of microwave filtering devices, by using non-homogeneous lines (NL) with the selection of the appropriate wave impedance W. For this purpose, equivalent NL substitution circuits were created in the region of resonant and antiresonant frequencies, and four-pole matrices of the transmission line were determined, whose matrix of impedances and admittances does not have partial poles, and the transmission admittance and transmission impedance do not have zeros. BPF prototypes were synthesized with two parallel plumes based on a closed homogeneous line and one plume based on three NLs. A band-pass filter with an extended blocking band was implemented, and its amplitude-frequency characteristics were obtained. The use of NLs as resonators allows the choice of wave impedance to increase the blocking band of the BPF compared to the BPF on resonators based on homogeneous lines.
The Analytical Approach to Evaluate the Bit Error Rate Performance of PLC System in Presence of Cyclostationary Non-White Gaussian Noise Rahman, M. M.; Alam, M. T.; Ashiquzzaman, M.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5386

Abstract

In a Powerline Communication (PLC) system, improper connections of associated hardwires can lead to the generation of unwanted RF signals, overriding the transmitted signal and producing undesired RF spurious signals. Noise in powerlines also arises from the corona effect, voltage impulses, and arcs occurring in transmission and distribution lines, significantly compromising the integrity of the PLC network. Analysis indicates that powerline noise exhibits a non-white cyclostationary characteristic. Due to its severity, PLC noise is categorized primarily as background noise and impulsive noise. This paper evaluates the characteristics of a powerline network under severe noisy conditions, particularly focusing on Cyclostationary Non-White Additive Gaussian Noise (CNWAGN) across broadband and narrow frequency communication channels. Accordingly, an analytical model is developed to specifically examine the bit error rate (BER) in environments affected by non-white additive Gaussian noise. BER and receiver sensitivity are also assessed for various bit rates using MATLAB simulations, demonstrating performance in terms of BER. This analytical model provides a straightforward method to evaluate results across different bit error rates in frequency-dependent and independent scenarios, surpassing traditional approaches. It proves highly effective in assessing Powerline Communication System performance, with analytically derived outcomes closely aligning with simulation results.
Classifications of Arabic Customer Reviews Using Stemming and Deep Learning Khelil, Hawraa Fadhil; Ibrahim, Mohammed Fadhil; Hussein, Hafsa Ataallah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5452

Abstract

With the emergence of AI text-based tools and applications, the need to present and investigate text-processing tools has also been raised. NLP tools and techniques have developed rapidly for some languages, such as English. However, other languages, like Arabic, still need to present more methods and techniques to present more explanations. In this study, we present a model to classify customer reviews which are written in Arabic. The HARD dataset is used to be adopted as the dataset. Three Deep Learning classifiers are adopted (CNN, LSTM, RNN). In addition to that, three stemmers are used as text processing techniques (Khoja, Snowball, Tashaphyne). Furthermore, another three feature extraction methods were utilized (TF-IDF, N-gram, BoW). The results of the model presented several explanations. The best performance resulted from using (CNN+ Snowball+ N-Gram) with an accuracy of (%93.5). The results of the model stated that some classifiers are sensitive toward using different stemmers, also some accuracy performance can be affected if there are different feature extraction methods used. Either stemming of feature extraction has an impact on the accuracy performance. The model also proved that the dialectal language could cause some limitations since different dialects can give conflict meaning across different regions or countries. The outcomes of the study open the gate towards investigating other tools and methods to enrich Arabic natural language processing and contribute to developing new applications that support Arabic content.
Advanced Multimodal Emotion Recognition for Javanese Language Using Deep Learning Arifin, Fatchul; Nasuha, Aris; Priambodo, Ardy Seto; Winursito, Anggun; Gunawan, Teddy Surya
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5662

Abstract

This research develops a robust emotion recognition system for the Javanese language using multimodal audio and video datasets, addressing the limited advancements in emotion recognition specific to this language. Three models were explored to enhance emotional feature extraction: the SpectrogramImage Model (Model 1), which converts audio inputs into spectrogram images and integrates them with facial images for emotion labeling; the Convolutional-MFCC Model (Model 2), which leverages convolutional techniques for image processing and Mel-frequency cepstral coefficients for audio; and the Multimodal Feature-Extraction Model (Model 3), which independently processes video and audio features before integrating them for emotion recognition. Comparative analysis shows that the Multimodal Feature-Extraction Model achieves the highest accuracy of 93%, surpassing the Convolutional-MFCC Model at 85% and the Spectrogram-Image Model at 71%. These findings demonstrate that effective multimodal integration, mainly through separate feature extraction, significantly enhances emotion recognition accuracy. This research improves communication systems and offers deeper insights into Javanese emotional expressions, with potential applications in human-computer interaction, healthcare, and cultural studies. Additionally, it contributes to the advancement of sophisticated emotion recognition technologies.
Efficient Invisible Color Image Watermarking Based on Chaos Samia, Belkacem; Noureddine, Messaoudi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5710

Abstract

Several difficulties are faced in developing a robust and transparent color image watermarking system, which requires the blending of the human visual system (HVS) during its design. Therefore, employing masks that take into account the features of HVSs has become a very effective tool for boosting robustness requirements without significant alterations in image imperceptibility. The present article offers watermarking strategy for colored images employing a reverse self-reference image in conjunction with the HVS constraint. A color image first undergoes conversion through the Red, Green, and Blue (RGB) format to the National Television Systems Committee (NTSC) space. The reference image is derived from the luminance channel through the discrete wavelet transform (DWT) domain. However, the chaotic map serves to generate the watermark, and a 2D torus automorphism is subsequently used to scramble the watermark. Therefore, the watermark is scrambled and placed in the reference image. Moreover, the detecting phase involves the host image, where the reference image is extracted from both the host and the image with a watermark, and the correlation is subsequently used to assess the similarity between the retrieved and the introduced watermark. The proposed watermarking scheme can retain the watermarked image's perceptibility justified by the PSNR. In addition, it achieves high robustness to withstand a wide array of attacks. 
The Circulatory System in an Electromagnetic Field Savenko, Elena; Belov, Alexander
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5503

Abstract

The article deals with the interaction of two electromagnetic fields: the intrinsic electromagnetic field of the elements of circulatory system and the external electromagnetic field of environment. A model of the circulatory system is proposed that allows for a systematic assessment of the impact of electromagnetic fields on the cardiovascular system. The model is based on the biophysical and bioelectrical properties of the elements of the cardiovascular system and the central nervous system. The article considers issues related to the behavior of the vessels of the arterial part of the vascular bed: the capillary network, arterioles and large arteries in an electromagnetic field. The dynamics of myocardial behavior in two phases is clearly illustrated using a two-circuit electrical circuit. The change in the dynamics of the state of an elementary section of the vascular bed over time is estimated using a system of equations based on Hooke's law. The possible mechanism of human behavioral character in unfavorable environmental conditions is analyzed based on the principle of adequate design, which is presented in the diagram of the step-by-step impact of the external environment and its influence on the behavior of the cardiovascular system depending on the intensity of the impact.
Regulation of Active and Reactive Powers in Doubly-Fed Induction Generators Utilizing Proportional-Integral and Artificial Neural Network Controllers Bouzidi, Mohammed; Nasri, Abdelfatah; Hafsi, Oussama; Faradji, Boubakar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5472

Abstract

In this paper, vector orientation and neural networks are used to simulate and regulate a Doubly Fed Induction Generator (DFIG) wind turbine. The aerodynamic turbine and DFIG dq models are developed. PI current regulation is used in vector control to separate active and reactive power control. To reproduce the PI response, training networks create a different neural vector control scheme. Comparative simulations confirm the effectiveness of both control methods in following set points and counteracting disturbances. The neural vector control scheme outperforms the PI scheme in managing short-term changes. In contrast to the PI control, it has quicker response times for both rising and settling. Neural vector control enables precise and rapid tracking of electromagnetic torque. Neural vector control could improve the performance of DFIG wind turbines because it has an adaptive architecture that lets it respond well to changes in parameters and maintain its accuracy over time. Additional investigation is needed to improve neural network training techniques and incorporate them with conventional control systems.
Wireless Need Sharing and Home Appliance Control for Quadriplegic Patients Using Head Motion Detection Via 3-Axis Accelerometer Abdul Kader, Mohammed; Orna, Sadia Safa; Tasnim, Zarin; Hassain, Md Mehedi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5593

Abstract

Patients who are quadriplegic are immobile in all four limbs. Quadriplegic patients with low voices struggle to communicate their needs to family members or caregivers, requiring assistance to use household items like fans and lights. This paper presents an electronic system designed to enhance the quality of life of quadriplegic patients by enabling them to share needs, manage household items, and monitor their health. The quadriplegic patient can move their head. In the proposed system, an accelerometer sensor placed on the patient’s forehead to record head movement, which is processed to detect and share needs or operate home appliances. The system consists of two units: one in the patient’s bed and another in a common place at home. Both communicate through Bluetooth. By moving head in the right direction, patients can share needs like water, rice, snacks, sickness or washroom. The common unit notifies caregivers through a matrix display and makes sounds with a buzzer. Patients can also control specific household appliances through left-head movements. The system also features a pulse oximeter sensor for monitoring heart rate and oxygen saturation. A prototype of the system has been developed and tested, and it is functioning smoothly. This system will free the quadriplegic patients from dependence on others and make their lives easier.
Enhancing Accuracy for Classification Using the CNN Model and Hyperparameter Optimization Algorithm Quoc, Dai Nguyen; Tran, Ngoc Thanh
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5545

Abstract

The Convolutional Neural Network (CNN) is a widely employed deep learning model, particularly effective for image recognition and classification tasks. The performance of a CNN is influenced not only by its architecture but also critically by its hyperparameters. Consequently, optimizing hyperparameters is essential for improving CNN model performance. In this study, the authors propose leveraging optimization algorithms such as Random Search, Bayesian Optimization with Gaussian Processes, and Bayesian Optimization with Treestructured Parzen Estimators to fine-tune the hyperparameters of the CNN model. The performance of the optimized CNN is compared with traditional machine learning models, including Random Forest (RF), Support Vector Classification (SVC), and K-Nearest Neighbors (KNN). Both the MNIST and Olivetti Faces datasets are utilized in this research. In the training procedure, on the MNIST dataset, the CNN model achieved a minimum accuracy of 97.85%, surpassing traditional models, which had a maximum accuracy of 97.50% across all optimization techniques. Similarly, on the Olivetti Faces dataset, the CNN achieved a minimum accuracy of 94.96%, while traditional models achieved a maximum accuracy of 94.00%. In the training-testing procedure, the CNN demonstrated impressive results, achieving accuracy rates exceeding 99.31% on the MNIST dataset and over 98.63% on the Olivetti Faces dataset, significantly outperforming traditional models, whose maximum values were 98.69% and 97.50%, respectively. Furthermore, the study compares the performance of the CNN model with three optimization algorithms. The results show that integrating CNN with these optimization techniques significantly improves prediction accuracy compared to traditional models.
The Efficiency of HEVC/H.265, AV1, and VVC/H.266 in Terms of Performance Compression and Video Content Boumehrez, Farouk; Sahour, Abdelhakim; Djellab, Hanane; Maamri, Fouzia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5336

Abstract

In recent times, there has been a significant focus on digital compression. The purpose of this study is to undertake a comparative evaluation and examination of the efficacy of the latest standards, namely HEVC, AVI, and its successor VVC. The determination of which standard to utilize relies heavily on factors such as the inherent characteristics of the video, its functionalities, quantization parameters, image quality, as well as the size and video content, this latter, is often classified by spatio-temporal complexity using spatial and temporal information (SI/TI). In reality, they are mostly used for original video sources. The efficiency of encoding original video sources is unknown. The results show that each standard has characteristics that sometimes make it superior to others. In addition, We observe that By understanding how SI and TI affect encoding efficiency, we will be able to better optimize the encoding process and reduce the amount of data that needs to be stored, transmitted, and processed. This could help to reduce the amount of time and energy required to encode video content, as well as reduce the amount of storage space needed to store it. Compared to H.265/HEVC, AV1 is more efficient at compressing HD and FHD video, and more efficient for SD video. In addition, experiments show that VVC/H.266 has higher compression efficiency.

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