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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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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.
Malware Classification Using Machine Learning and Dimension Reduction Techniques on PE File Data Pradipta, Arif Harsa; Wulandhari, Lili Ayu
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.v12i3.5657

Abstract

The digital transformation has enhanced efficiency, transparency, and accessibility but has also led to a notable increase in cyber incidents, including malware attacks. According to the 2022 annual report from the Honeynet Project by the National Cyber and Encryption Agency, Indonesia experienced over 370 million cyber attacks, with 800,000 of these being malware attacks. The increasing complexity of Portable Executable files further complicates accurate classification in machine learning models. This research aims to develop an effective malware detection approach using machine learning classifiers—Random Forest, XGBoost, and AdaBoost—on raw feature dataset and integrated feature dataset. Dimension reduction techniques such as Principal Component Analysis and Linear Discriminant Analysis were utilized to enhance classification efficiency. The results demonstrated that Random Forest and XGBoost consistently outperformed AdaBoost, particularly in classifying ransomware, achieving recall values ranging from 0.72 to 0.85 and F1-scores from 0.74 to 0.81 For the trojan class, both Random Forest and XGBoost achieved recall values ranging from 0.96 to 0.97, with corresponding F1-scores between 0.95 and 0.97. Both classifiers maintained high precision, recall, and F1-scores across all malware classes, even with reduced feature sets.
Feature Optimization for Machine Learning Based Bearing Fault Classification Mohiuddin, Mohammad; Islam, Md Saiful; Uddin, Jia
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.v12i3.5671

Abstract

The most critical and essential parts of rotating machinery are bearings. The main problem of the bearing fault classification is to select the fault features effectively because all extracted features are not useful, and the high-dimensional features give poor performances and slow down the training process. Due to the effective feature selection problem, the bearing fault diagnosis method does not achieve a satisfactory result. The main goal of this paper is to extract the effective fault features with an optimization technique to classify the bearing faults using machine learning algorithms. Since wavelet entropy can determine complexity and degree of order of a vibration signal, this research uses it in features optimization.  The proposed wavelet entropy-based optimization technique reduces the dimensionality of input, elapsed time and raises the learning process. Four Machine learning algorithms (naïve Bayes, support vector machine, artificial neural network and KNN) are applied to classify the bearing faults using the optimized features.    To evaluate the proposed method, Case Western Reserve University’s (CWRU’s) bearing dataset is used which consists of three types of bearing faults. The accuracy and robustness of the bearing fault classification are tested by adding noise to the vibration raw signals at various levels of Signal-to-Noise Ratio (SNR). Experimental results show that the proposed method is very highly reliable in detecting bearing faults compared to the conventional methods.
Solving Dynamic Combined Economic Environemental Dispatch Problem with Renewable Energies and Constraints Using Gorilla Troops Optimizer Abrouche, Amel; Bouzeboudja, Hamid; Dahmani, Kaouthar Lalia; Naama, Bakhta
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.v12i3.5486

Abstract

The primary goal is to optimize the hourly allocation of power generation outputs by minimizing operational costs, pollutant emissions, and transmission losses, and ensuring compliance with a range of equality and inequality constraints. To tackle this challenge, a novel metaheuristic algorithm inspired by gorilla’s behavior is proposed. Gorilla Troops Optimizer (GTO) was applied to 5- and 10-generator unit systems, integrating variable wind and solar energies over a day with varying load demands. To demonstrate the effectiveness of the GTO algorithm in handling the hybrid dynamic combined economic and environmental dispatch problem, including equality constraints, transmission losses, valve-point effects, prohibited operating zones, ramp rates, and power limits, its performance was compared with other optimization techniques. The findings indicate that GTO provides the optimal scheduling of power generators, leading to significant reductions in daily operational costs and emissions with high percentages. Moreover, the integration of renewable energy significantly reduces pollutant gas emissions, fuel costs, and transmission losses, while meeting all imposed constraints. This research positively contributes to enhancing the reliability of power supply systems, while simultaneously reducing environmental pollution, transmission losses, and fuel costs.

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