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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Enhancement of energy and spectral efficiency for mm-wave based 5G communication network Gaikwad, Vishakha; Naik, Ashwini
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6380-6388

Abstract

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.
Situational judgment test measures administrator computational thinking with factor analysis Indrawati, Cicilia Dyah Sulistyaningrum; Permansah, Sigit; Ninghardjanti, Patni; Subarno, Anton; Winarno, Winarno; Rusmana, Dede
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2088-2099

Abstract

Computational thinking skills (CTS) play a crucial role across diverse domains, involving a thinking process that allows the execution of solutions by information processing agents. Measuring the level of CTS becomes essential to ensure that administrators effectively leverage technology. However, finding suitable instruments to measure and justify CTS levels in administration can be challenging. The selection of situational judgement test (SJT) is supported by its validity and reliability in assessing attributes, including professionalism. The instrument’s development includes face validity, discriminant validity (using Pearson correlation and Cronbach’s alpha), and exploratory factor analysis (EFA). The study involved 111 undergraduate administration students from various Indonesian universities, and data were collected in 2023. Following a discriminant validity analysis, ten items were eliminated, while 23 met the criteria with p0.185. Subsequently, EFA yielded 16 items forming seven components, covering algorithmic thinking, problem-solving, technology literacy, problem abstraction, problem reformulation, data management in administration technology, and administrative data presentation with loading factor variations (0.421-0.868). The final instrument, consisting of 16 valid items and seven components, effectively evaluates the level of administrator computational thinking skills (ACTS) among administration students.
Comparative evaluation of centralized and decentralized solar street lighting systems Joviancent, Kenzie; Halim, Levin; Naa, Christian Fredy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4869-4878

Abstract

Inadequate lighting can hinder outdoor activities such as traffic or pedestrian access. Solar street lighting system is planned to provide sufficient lighting for roads lacking proper illumination. DIALux software uses simulation to determine the lamp power and pole specifications, followed by applying formulas to establish component specifications. In this research, a performance comparison based on voltage drop and power losses will be conducted for solar street lighting systems with decentralized and centralized systems. With a road length of 130 meters and a width of 5 meters, simulations were performed for each variable of lamps (10, 12, 19, and 30 Watt). The calculations show that 4 streetlights are needed, and simulation results indicated that the most suitable lamp power is 12 Watt. The analysis showed that the centralized and decentralized designs do not have voltage drops exceeding the applicable limit. However, the centralized design has higher power losses amounting to 3.68 Watt. Another advantage of the decentralized design is its independence, with each load powered by a separate solar panel, while the centralized design is vulnerable to the overall system. In conclusion, the decentralized design is more suitable for implementation after comparing the centralized and decentralized designs based on the voltage drop and power losses.
Predictive model for acute myocardial infarction in working-age population: a machine learning approach Urbano-Cano, Astrid Lorena; López-Mesa, Diana Jimena; Alvarez-Rosero, Rosa Elvira; Garces-Gomez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp854-860

Abstract

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population.
Internet of things-based electrical energy control and monitoring in households using spreadsheet datalogger Jannah, Misbahul; Hasibuan, Arnawan; Kartika, Kartika; Asran, Asran; Yunizar, Zara; Usrina, Nura; Nuryawan, Nuryawan; Almunadiansyah, Rizky
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3931-3941

Abstract

Today, the demand for electrical energy is paramount in various daily activities. Hence, individuals must be aware of the amount of electrical energy consumed to maintain the quality of electronic devices. Knowing the quality of electronic devices is essential since it can impact the performance and lifespan of electrical equipment. The value of electrical power is determined by the quality of electrical power and the number of hours. Monitoring electrical energy involves collecting or measuring data to assess the current level of energy consumption. The author is interested in researching the use of Datalogger Spreadsheets to monitor and gather real-time information on energy use, which is made possible through integration with internet of things (IoT) and microcontrollers. Through data analysis and observation, solutions to existing problems are sought by comparing and matching data. Monitoring daily energy usage in a home setting produces output data that can be viewed directly and remotely with real-time results. This tool is expected to address current issues.
Study of the characteristics of broadband matching antennas for fifth-generation mobile communications based on new composite materials Nakisbekova, Balausa; Yerzhan, Assel; Boykachev, Pavel; Manbetova, Zhanat; Imankul, Manat; Shener, Anar; Yermekbaev, Muratbek; Dunayev, Pavel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2885-2895

Abstract

The presented research aims to analyze in detail the characteristics of broadband matching antennas specifically designed for 5G mobile communications applications, with an emphasis on innovative composite materials. The study focuses on a compact planar loop antenna designed for use on smartphones, covering the LTE/WWAN frequency bands 824 to 960 MHz, 1,710 to 2,690 MHz, and 3,300 to 3,600 MHz for full coverage of modern 5G networks. Experimental and numerical methods are used to broadly analyze the frequency range associated with 5G networks. The features of the use of composite materials in the implementation of antenna devices in 5G technologies are noted. A broadband matching circuit (BMC) with elements with lumped parameters and a reduced sensitivity invariant has been synthesized. A 3D model of the adaptive selective surface controller (SSC) was developed using CST Studio. The study results highlight the benefits of new composite materials in improving the performance of 5G antennas. This research makes a significant contribution to the development of 5G technologies by optimizing antenna design for efficient data transmission in modern mobile networks and can be a valuable resource for engineers and designers working in this field.
A comparative study of long short-term memory based long-term electrical load forecasting techniques with hyperparameter optimization Mani, Geetha; Seetharaman, Suresh; Kandasamy, Jothinathan; Ladha, Lekshmy Premachandran; Mohandas, Anish John Paul; Sivasubramoniam, Swamy; Renugadevi, Valarmathi Iyappan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7080-7089

Abstract

Long-term load forecasting (LTLF) is crucial for reliable electricity supply, infrastructure planning, and informed energy policies, ensuring grid stability and efficient resource allocation. Traditional methods, like statistical models and expert judgment, rely on historical data but may struggle with dynamic changes in technology, regulations, and consumer behavior. Addressing challenges such as economic uncertainties, seasonal variations, data quality, and integrating renewable energy requires advanced forecasting models and adaptive strategies. This research aims to develop an efficient LTLF model for the Coimbatore region in Tamil Nadu, India, using long short-term memory (LSTM) networks. While LSTM has limitations in capturing long- term dependencies and requires high data quality and complex management, optimizing hyperparameters, including through the opposition-based hunter- prey optimization (OHPO) technique, is explored to enhance its predictive performance. The results show that the proposed OHPO-configured LSTM model for LTLF achieves superior performance compared to other techniques, with a mean square error (MSE) of 0.25, root mean square error (RMSE) of 0.5 and mean absolute percentage error (MAPE) of 0.27. This research underscores the significance of improving LTLF precision for informed decision-making in infrastructure planning and energy policy formulation.
Predicting churn with filter-based techniques and deep learning Quek Jia Yi, Vivian; Ying Han, Pang; Zheng You, Lim; Shih Yin, Ooi; How Khoh, Wee
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2135-2144

Abstract

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.
Dynamic voltage restoration using neural networks for grid-connected wind turbine Dahmane, Kaoutar; Bouachrine, Brahim; Imodane, Belkasem; Idrissi, Abdellah El; Benydir, Mohamed; Ajaamoum, Mohamed; Oubella, M'hand
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5018-5029

Abstract

Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality.
Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation Shetty Kirimanjeshwara, Raghavendra Mandara; Narasimha Prasad, Sarappadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp315-325

Abstract

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

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