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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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Design of Solar Home Charging for Individual Electric Vehicles: Case Study for Indonesian Household Nurwidiana, Nurwidiana; Nugroho, Dedi; Fatmawati, Wiwiek
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.v12i4.5078

Abstract

This study aims to examine the use of solar energy through Rooftop Photovoltaic (RPV) technology for solar-powered home charging of individual electric vehicles (EVs) in Indonesia. Simulations using HOMER Pro software are carried out to analyze both the energy and financial performance of the designed RPV system. According to the calculations, a 4 kW RPV system is required to meet the daily energy demand for EVs in the household sector. The off-grid RPV system design consists of 12 unit 325 wp PV panels, 36 units of 100AH battery as power storage, and a 4000-watt inverter to convert DC from RPV system into AC for battery charging. Simulation results from HOMER Pro software confirm that the designed RPV system can adequately supply the electricity needed for home charging, generating a total of 6449 Wh per year. With an annual energy consumption of approximately 4,190 kWh/year, the proposed system not only meets the daily energy needs of EVs but also provides excess power to be used by additional electrical equipment. Additionally, the proposed system can reduce 77.89 tons of CO2 emissions over the 25-year project lifespan.
Machine Learning-Driven Pre-Broadcast Video Codec Validation: Ensuring Seamless Television Transmission El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen
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.v12i4.5845

Abstract

This study addresses the critical challenge of ensuring uninterrupted television broadcasting by proactively detecting video codec errors, focusing on TV Laayoune, a prominent Moroccan channel. We developed a machine learningbased methodology that identifies incompatible codecs before they disrupt live broadcasts. The approach involves data collection from multiple sources, including TV Laayoune's archives, metadata extraction via FFmpeg, and a hybrid model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Integrated into the broadcasting pipeline, this model achieved a 95% accuracy rate, significantly enhancing broadcast reliability and operational efficiency. Additionally, we propose a user-friendly interface for real-time error detection, comprehensive workflow integration, and automated alerts. This innovative solution addresses common broadcast challenges, reducing operational risks and improving the viewer experience.
Improved Lung Sound Classification Model Using Combined Residual Attention Network and Vision Transformer for Limited Dataset Jurej, Muhammad; Roslidar, Roslidar; Yunida, Yunida
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.v12i4.5530

Abstract

According to WHO data, the prevalence of respiratory disorders is increasing, exacerbated by a shortage of skilled medical professionals. Consequently, there is an urgent need for an automated lung sound classification system. Current methods rely on deep learning, but limited lung sound data resulted in low model accuracy. The widely used ICBHI 2017 dataset has an imbalanced class distribution, with a normal class at 52.8%, wheezing at 27.0%, crackles at 12.8%, and combined wheeze and crackles at 7.3%. The imbalance of the dataset may affect the model's efficiency and performance in classifying lung sounds. Given these data limitations, we propose a hybrid model, combining residual attention network (RAN) and vision transformer (ViT), to construct an effective respiratory sound classification model with a small dataset. We employ feature fusion techniques between convolutional neural network (CNN) feature maps and image patches to enrich lung sound features. Additionally, our preprocessing involves bandpass filtering, resampling sounds to 16 kHz, and normalizing volume to 15 dB. Our model achieves impressive ICBHI scores with 97.28% specificity, 92.83% sensitivity, and an average score of 95.05%, marking a 10% improvement over state-of-the-art models in previous research.
Cyber Security Threat Prediction using Time-Series Data With LSTM Algorithms Hakim, Lukman; Wulandhari, Lili Ayu
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.v12i4.5648

Abstract

Cyber security remains a paramount concern in the digital era, with organizations and individuals increasingly vulnerable to sophisticated cyber-attacks. This study aims to develop and evaluate Long Short-Term Memory (LSTM) regression models to predict three types of cyber attacks: flood, spyware, and vulnerability. The LSTM algorithm is used to construct regression models for spyware, flood, and vulnerabilities within a firewall log dataset. The experiments demonstrate that preprocessing techniques such as normalization and standardization can positively impact model performance by reducing prediction errors and enhancing accuracy. The results of the experiments show that the model developed in this research exhibits potential in predicting cyber attacks. For the flood attack model, the best performance was achieved with an RMSE of 59.8810 and an R-Squared of 0.9214 after data standardization. The spyware attack model's best results were an RMSE of 133.9567 and an R-Squared of 0.7685 after standardization. In contrast, the vulnerability attack model showed limited improvement, with the best RMSE of 503.5521 and an R-Squared of 0.2358 after standardization. Moreover, real-time implementation and testing of these models in live network environments could validate their practical applicability and effectiveness.
Detection and Estimation of Schizophrenia Severity from Acoustic Features with Inclusion of K-means as Voice Activity Detection Function Alimi, Sheriff; Kuyoro, Afolashade Oluwakemi; Eze, Monday Okpoto; Akande, Oyebola
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5506

Abstract

Schizophrenia symptom severity estimation provides quantitative information that is useful at both the detection and treatment stages of the mental disorder, as the information helps in decision-making and improves the management of the illness. Very limited studies have been recorded for estimating the symptom severity as a regression task with machine learning, especially from speech recordings, which is the aim of this study coupled with detection. Acoustic features, which comprise frequency-domain and time-domain features, were extracted from 60 schizophrenia subjects and 59 healthy controls enrolled in this research. The acoustic features were used to train GridSearchCV-optimized XGBoost as a classifier. Three Multi-Layer Perceptron (MLP) networks, hyper-parameter-tuned by Bayesian Optimizer, were trained to predict the sub-type symptom severity from acoustic extracted features from the schizophrenia groups. The XGBoost classification model that discriminates between schizophrenia and healthy groups achieved a classification accuracy of 98.6%. The three MLP regression models yielded Mean Absolute Errors of 1.975, 2.856, and 1.555, as well as correlation coefficients of 0.888, 0.806, and 0.786 for predicting positive, negative, and cognitive symptom scores, respectively. Solution architecture for the deployment of the models for practical use was suggested
EfficientNet Model for Multiclass Classification of The Correctness of Wearing Face Mask Khadijah, Khadijah; Kusumaningrum, Retno; Rismiyati, Rismiyati; Sabilly, Nur
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5197

Abstract

A face mask is essential for protecting individuals from the entry of infectious or hazardous materials through the nose or mouth in specific situations. To optimize its protective function, it must be worn correctly. This research aims to develop a multiclass classification model, rather than a binary one, to assess the correctness of wearing face mask. The proposed model is designed to achieve high accuracy while maintaining efficiency, with a low number of model parameters. To this end, a deep convolutional neural network (CNN), specifically EfficientNet, is utilized. Experiments are conducted on the public MaskedFace-Net image dataset, which consists of four categories (correctly masked, uncovered chin, uncovered nose, and uncovered nose and mouth), using 3,000 randomly selected images from each category. The experiments test several EfficientNet models (B0-B3) and network hyperparameters (learning rate and dropout). The best accuracy of 0.99 is achieved by EfficientNet-B0 with a learning rate of 0.01 and a dropout rate of 0.2. The EfficientNet-B0 model outperforms other benchmark CNN models, including MobileNet-V3 and Inception-V3, despite having a slightly higher number of parameters than MobileNet-V3. This result demonstrates that the EfficientNet model is both accurate and efficient for multiclass classification of the correctness of wearing face mask.
IVFD: An Intelligent Video Forgery Detection Framework Leveraging InceptionV3 and GRU for Enhanced Forensics Bhargavi, Kumbham; Pasha, M Jahir; Kotoju, Rajitha; Vani, M. Sree
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6030

Abstract

Cloud computing-like services that are great at paying for and managing multimedia are fundamental technological innovations that have made it easier for individuals and organizations to adopt multimedia content. Thanks to social media, different people with different perspectives can voice their opinions and present data through photos and videos. However, video tampering is a significant issue because illegal modification of video content can easily mislead audiences and make it difficult for them to relate to reality. This is, therefore, a serious problem, as the consequences of video forgery are dire. Several image processing-based solutions have emerged to address video forgery. Artificial intelligence has recently allowed deep learning models to be trained extensively; hence, deep learning has been frequently used for video tampering detection. However, further work is still required to refine such models or develop hybrid models to improve the existing models' capabilities in identifying video forgeries and assisting digital forensics. We introduce a framework based on deep learning to automate the detection and localization of video forgeries. We offer a hybrid deep learning model that fuses Inception V3 with a Gated Recurrent Unit (GRU) as part of our framework. We also propose a new algorithm, Intelligent Video Forgery Detection (IVFD), to detect the forgeries and their invariants based on this hybrid model. Through empirical studies applied on a standard dataset, called the Deepfake Challenge dataset, we get an accuracy of 97.21%, which makes our hybrid deep learning model outperform many existing models. Since video content is prevalent in almost all applications in today's era, our design system should be laid on top of these applications, which can facilitate detecting the tampering of the videos and thereby contribute towards digital forensics.
Transfer Learning for Detecting Alzheimer’s Disease in Brain Using Magnetic Resonance Images Islam, Md. Monirul; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5069

Abstract

Alzheimer’s Disease (AD) is one of the most concerning diseases because the patients show very few symptoms at the earlier stages. Dementia is very common in patients who have suffered brain damage or those who have suffered from psychotic trauma. Patients who have a lot of age suffer the most from it. Magnetic resonance imaging (MRI) is widely used to clinically treat patients with Alzheimer’s. Currently, there is no known remedy for the disease. We can only identify and try to give the proper medications to give some relief to patients. In this study, we have collected MRI data from patients with 4 different stages of Alzheimer’s. The purpose of this paper is to build a model to securely detect these stages for the betterment of medical science. We implemented a transfer learning method with state-of-the-art models such as ResNet50, DenseNet121, and VGG19. We proposed our method with these models which have pre-trained weights of “ImageNet”. The layers that we added are our novelty. We were able to achieve 97.70% accuracy on our best pre-trained model with an F1 score of 97% and a precision of 97% on our test data.
Improving Channel Gain of 6G Communications Systems Supported by Intelligent Reflective Surface Alsahlanee, Abbas Thajeel Rhaif; Al-Safi, Jehan Kadhim Shareef
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6169

Abstract

The 6G wireless communication networks may use intelligent reflecting surfaces (IRS). It can enhance energy efficiency (EE). The IRS can enhance wireless communication by selectively reflecting incident signals in favorable directions. A potential method to improve the efficacy of wireless channels is to use a software-controlled metasurface that reflects signals when the direct transmission line from the source to the destination is insufficient. The IRS may redesign the environment to facilitate radio signal transmission. The decrease in channel gain in 6G communications networks using multiple reflective elements of the IRS is one of the challenges. This study seeks to propose a solution to enhance the channel gain and performance of the IRS in 6G communication systems. The research aimed to improve channel gain in assisted-IRS 6G communication systems by artificial intelligence algorithm (DS-PSO: dynamic and static particle swarm optimization). This study's technique enhances the effectiveness of aided-IRS communication methods. The simulation results of the optimized IRS model proposed in this paper show a significant improvement in channel gain compared to the results of previous studies.
Simulation-Based Evaluation of Dense Convolutional Neural Networks for Skin Cancer Detection Behara, Kavita; Bhero, Ernest; Agee, John Terhile
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5825

Abstract

Skin cancer, particularly melanoma, poses significant challenges to public health, with early detection being critical for effective treatment. Traditional diagnostic methods often fall short, particularly in resource-limited settings. In response, artificial intelligence (AI) techniques, especially deep learning models, have emerged as promising tools for automated skin cancer detection. This study evaluates the performance of Dense Convolutional Neural Networks (DCNNs) in classifying and detecting skin lesions, leveraging simulation-based approaches to assess the effectiveness of various AI models. Utilizing datasets such as HAM10000 and ISIC2017, which contain a wide variety of skin types and lesion stages, the models were trained and tested using key performance metrics such as accuracy, precision, recall, and F1-score. The results shows that DCNNs outperformed traditional machine learning techniques like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees (DT), demonstrating superior accuracy, generalization ability, and efficiency in handling large, imbalanced datasets. The simulation-based approach provided insights into the ability of DCNN models to manage dataset inconsistencies and class imbalances, showcasing their potential as robust tools for skin cancer detection. These findings highlight the ability of AI in advancing dermatological diagnostics, offering more timely and accurate detection, and potentially improving patient outcomes

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