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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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Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation Daniel Febrian Sengkey; Angelina Stevany Regina Masengi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4376

Abstract

Numerous approaches have been developed to group learners’ behavior in an online/blended learning environment. However, most clustering analyses in this particular field only consider numeric features despite the existence of categoric features that are found important in other studies. In this study, we compare K-Means and K-Prototypes algorithms to cluster learners’ behavior in a flipped classroom implementation. From the model selection, we found that the model produced by the K-Prototypes algorithm — which included categoric features — is a better one. The statistical analysis of the clustering results of the selected K-Prototypes model shows significant differences in most of the inter-cluster comparisons, implying a good separation of the data. More importantly, we can identify the behavior in each cluster which then can be used to help learners in achieving better results in learning.
Day Ahead Energy Consumption Forecasting Through Time-Series Neural Network R. Reshma Gopi; Chitra Annamalai
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4280

Abstract

Demand response management through appliance scheduling can effectively decrease electricity bills. Similarly, it can decrease the peak demand of both consumers and the utility grid. However, prior knowledge of the load profile of the consumer is required for effective appliance scheduling. This work developed a novel time-series forecasting model within the shallow neural network framework to predict the load curve for optimal planning of demand response by a nonlinear autoregressive network with exogenous input network. The algorithms such as Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization was applied to train the model. The results were compared with conventional seasonal autoregressive integrated moving average statistical model and long short-term memory network. The results indicated that the optimum methodology is a nonlinear autoregressive network with exogenous inputs using the Bayesian regularisation algorithm, which has the lowest MSE value in the training and testing phases of 0.0031 and 0.0029, respectively. It is practical to continue designing artificial neural networks to analyze hourly load consumption in the context of the positive outcomes acquired.
A Review on VANET Security: Future Challenges and Open Issues Md. Julkar Nayeen Mahi; Sudipto Chaki; Esraq Humayun; Hafizul Imran; Alistair Barros; Md Whaiduzzaman
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4295

Abstract

Vehicular Adhoc Network (VANET) is an established technology that is well-suited for emerging technologies such as the Internet of Vehicles (IoV) and Unmanned Aerial Vehicles (UAVs). However, while VANET offers improved methods for addressing contemporary technology, it also presents significant challenges in providing adequate security measures for intended access. VANET operates on multiple execution platforms, such as roadside units, vehicle-tovehicle, vehicle-to-device, and vehicle-to-everything (V2X) communication. As a result, VANET must establish robust security measures for future purposes and strengthen protocol authentications to ensure secure data delivery and network-wide execution. In this work, we provide an overview of some of the recent security problems faced by VANET to raise awareness among developers and engineers about the specific security needs of VANET and how to avoid errors or intrusions when deploying VANETs in cities and urban areas. We cover topics such as the classification of security attacks, standard or security protocol problems and solutions, and the best feasible security criteria for extended VANETs. Finally, we discuss open issues and future VANET security developments or concerns.
Soft Robots: Implementation, Modeling, and Methods of control Shahad AbdulAdheem Al-Ibadi; Loai Ali Talib Al Abeach; Mohammed Abd Ali Al-Ibadi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4288

Abstract

Soft robotics is a branch of robotics that focuses on technologies with physical features that are like those of live biological creatures. Additionally, they have many details that are hard, if not impossible, to realize with traditional robots which are composed of solid materials. This study concentrates on the current expansion of soft pneumatic actuators for modern soft robotics in recent years, with an emphasis on three areas: Implementation of soft robots, Modeling, and Methods of control systems. Therefore, numerous soft robotic designs and ways to make them suitable for medical, manufacturing, and agricultural applications have been presented. Moreover, a set of functional and technological aspects have been given to review models similar to human hand functionality and motions. To realize the advanced soft robotic hand manipulation function, robotic hands must be equipped with tactile sensing, that sensing is required to provide continuous data on the volume and direction of forces at all contact locations. The research examines achievements in material science, actuation, sensing techniques, and manufacturing technologies, as well as how to model and control a soft robot's motion, all of which are scientifically challenging and, more importantly, practical.
IoT-based Smart Campus Monitoring Based on an Improved Chimp Optimization-Based Deep Belief Neural Network S. Sebastin Antony Joe; S. J. Jereesha Mary; Ronald S. Cordova; Haydar Sabeeh Kalash; Ali Al-Badi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4410

Abstract

Internet of Things (IoT) is a fast emerging technology that gained momentum steadily and shaped the future of the smart world. It has been created from the curiosity of human beings to provide comfortable and connected lifestyles with the mitigation of labor and therein promptly reduces the errors. This led to the usage of smart devices in everyday activities and thus enhances the efficacy of all smart applications. Smart applications include smart farming, healthcare, smart grid, smart city, and more. The application of IoT in monitoring the smart campus is an inevitable one to monitor the attendance of students and monitoring other activitieson the campus to protect the students and improve the education standards. Most education institutes use smart classrooms to achieve the aforementioned quality. Smart classrooms include audio-visual aids, multimedia, and smart boards along with these it is ineluctable to monitor the activities such as students’ attendance, analyzing the students-faculty performance, and content deliveries. To record the students’ attendance automatically we propose a Bluetooth-enabled IoT smart system for the positing of students with low energy utilization. The attendance can be recorded in the cloud environment by the Received signal strength indicator (RSSI). To achieve this we propose a novel IoT-based Deep Belief Neural Network (DBN) based Improved Chimp Optimization algorithm (ICO) for monitoring the attendance and positioning of the students’. An experimental study is conducted on Raspberry Pi with the deployment of Python and shows that our proposed approach provides better accuracy even with high interference signals.
Influence of Renewable Energy Sources on Day Ahead Optimal Power Flow Based on Meteorological Data Forecast Using Machine Learning: A case study of Johor Province Haider Jouma Touma; Muhamad Mansor; Muhamad Safwan Abd Rahman; Hazlie Mokhlis; Yong Jia Ying
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4115

Abstract

This article investigates a day ahead optimal power flow considering the intermittent nature of renewable energy sources that involved with weather conditions. The article integrates the machine learning into power system operation to predict precisely day ahead meteorological data (wind speed, temperature and solar irradiance) that influence directly on the calculations of generated power of wind turbines and solar photovoltaic generators. Consequently, the power generation schedulers can make appropriate decisions for the next 24 hours. The proposed research uses conventional IEEE -30-bus as a test system running in Johor province that selected as a test location. algorithm designed in Matlab is utilized to accomplish the day ahead optimal power flow. The obtained results show that the true and predicted values of meteorological data are similar significantly and thus, these predicted values demonstrate the feasibility of the presented prediction in performing the day ahead optimal power flow. Economically, the obtained results reveal that the predicted fuel cost considering wind turbines and solar photovoltaic generators is reduced to 645.34 USD/h as compared to 802.28 USD/h of the fuel cost without considering renewable energy sources. Environmentally, CO2 emission is reduced to 340.9 kg/h as compared to 419.37 kg/h of the conventional system. To validate the competency of the whale optimization, the OPF for the conventional system is investigated by other 2 metaheuristic optimization techniques to attain statistical metrics for comparative analysis.
A Five Level Modified Cascaded H-Bridge Inverter STATCOM for Power Quality Improvement Prashant Kedarnath Magadum; Sangmesh Sakri
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4430

Abstract

Multilevel converters have received serious attention on account of their capability of high voltage operation, high efficiency, and low electromagnetic interference. It has many advantages compared to conventional two-level inverters such as high dc-link voltages, reduced harmonic distortion, fewer voltage stresses, and low electromagnetic interferences. The multilevel converters have been used for STATCOM widely as it can improve the power rating of the compensator to make it suitable for medium or high-voltage high power applications. While deploying multilevel STATCOMs, designer’s role is to reduce the number of switching devices since, the total switching losses are proportional to the number of switching devices. The reduction in the count of switching devices also reduces the size and cost. In this paper, a five-level modified cascaded H-bridge inverter STATCOM is proposed for mitigation of harmonics. Modified Five-level CHB configuration is the most suitable as with lesser number of switches, give better performance resulting in a compact system. The PQ theory-based controller is developed for control of STATCOM operation. MATLAB simulation results are presented to demonstrate mitigation of harmonics.
VGG19+CNN: Deep Learning-Based Lung Cancer Classification with Meta-Heuristic Feature Selection Methodology Bhagya Lakshmi Nandipati; Nagaraju Devarakonda
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4394

Abstract

Lung illnesses are lung-affecting illnesses that harm the respiratory mechanism. Lung cancer is one of the major causes of death in humans internationally. Advance diagnosis could optimise survivability amongst humans. This remains feasible to systematise or reinforce the radiologist for cancer prognosis. PET and CT scanned images can be used for lung cancer detection. On the whole, the CT scan exhibits importance on the whole and functions as a comprehensive operation in former cancer prognosis. Thus, to subdue specific faults in choosing the feature and optimise classification, this study employs a new revolutionary algorithm called the Accelerated Wrapper-based Binary Artificial Bee Colony algorithm (AWBABCA) for effectual feature selection and VGG19+CNN for classifying cancer phases. The morphological features will be extracted out of the pre-processed image; next, the feature or nodule related to the lung that possesses a significant impact on incurring cancer will be chosen, and for this intention, herein AWBABCA has been employed. The chosen features will be utilised for cancer classification, facilitating a great level of strength and precision. Using the lung dataset to do an experimental evaluation shows that the proposed classifier got the best accuracy, precision, recall, and f1-score.
Automatic Caption Generation for Aerial Images: A Survey Parag Jayant Mondhe; Manisha P. Satone; Gajanan K. Kharate
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4342

Abstract

Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed.
Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification Arselan Ashraf; Teddy Surya Gunawan; Fatchul Arifin; Mira Kartiwi; Ali Sophian; Mohamed Hadi Habaebi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4449

Abstract

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition.

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