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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 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Pedestrian flow prediction in commercial avenue Benhadou, Marwane; Gonnouni, Amina El; Lyhyaoui, Abdelouahid
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.pp5848-5857

Abstract

Mobility plans are one of the most important management tools for city development and an important factor for society and economic growth, where pedestrians are the end goal of any mobility plan. Human behavior is generally unpredictable, and many attempts have been interested at pedestrians' mobility in urban environments, both microscopic and macroscopic (flow, density, and speed) levels. The objective of pedestrian traffic flow prediction is to predict the number of pedestrians at the next moment. Assisting operators and city managers in making decisions in urban environments such as emergency support systems, and quality-of-service evaluation. This study aims to model and predict bi-directional pedestrian flow in a commercial avenue, based on two essential stages, data collection through video recording over two months (pedestrian flow) and data analysis using machine learning algorithms that provide a lower error and a higher accuracy rate. Two metrics were selected as basic measures to evaluate the model performances, root mean square error (RMSE) and coefficient of determination R2. Artificial neural network (ANN) gives a little better performance and fitness.
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.
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.
Machine learning-driven stock price prediction for enhanced investment strategy Guennioui, Omaima; Chiadmi, Dalila; Amghar, Mustapha
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.pp5884-5893

Abstract

Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions.
Supply and demand of ecosystem service provision in relation to dynamics land-cover changes: a remote sensing and geospatial analysis in Sukabumi Regency Fitriani, Ananda; Dimyati, Muhammad; Zulkarnain, Faris
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.pp5728-5737

Abstract

The rate of population growth in Sukabumi Regency continues to grow, along with the increasing need for food. This population growth, combined with the constant changes in land cover can reduce the productivity of environment in providing natural capital for food availability. Therefore, this study aimed to examine the condition of ecosystem service provision for a decade in Sukabumi Regency due to changes in land cover. In general, the efficient use of remote sensing method and geographic information systems to monitor ecosystem services had received widespread recognition. Following this scenario, the current study used geospatial analysis with dasymetric method which was integrated with supply and demand formulas for ecosystem services provision, food availability, and remote sensing. Geographic information system was also used for land cover interpretation data. The results showed that three districts in Sukabumi Regency, namely Cicurug, Cibadak, and Cicantayan, had “exceeded” condition when the environmental condition already passed the threshold or were unable to support population's needs. Meanwhile, the other districts have “not exceeded” condition, when the environmental conditions were still able to fulfill the needs of population. Finally, the changes in agricultural land cover had a significant influence on the condition of ecosystem services.
Exploring the frontiers of trajectory outlier detection: an in-depth review and comparative analysis Chakri, Sana; Mouhni, Naoual; Ennaama, Faouzia
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.pp5984-5997

Abstract

This paper provides a review and comparative analysis of trajectory outlier detection methods. It presents the definition of outliers in trajectory data and the existing types to further examine the advanced approaches. Basic steps for detecting an outlier, which include data preprocessing, feature extraction, modeling, and similar, have been presented. Moreover, advanced methods such as autoencoders and the use of deep learning for outlier detection have been explored. In the end, this paper evaluates the techniques and compares them using common metrics, mainly focusing on the techniques based on autoencoders or deep learning. It covers applications in real life and practice along with any limitations, challenges, and perspective ideas for the future. Ultimately, it can be a useful resource for expanding the understanding of domain researchers and practitioners.
Prediction of student performance at polytechnic using machine learning approach Hutajulu, Kristina; Wulandhari, Lili Ayu
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.pp5356-5365

Abstract

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and non-informatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.
Development and assessment of solar radiation forecasting models based on operational data Suwarno, Suwarno; Cahyadi, Catra Indra; Sukarwoto, Sukarwoto; Napitupulu, Janter
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.pp4838-4845

Abstract

Operational forecasting of solar radiation is critical for better decision-making by solar energy system operators, due to the variability of energy resources and demand. Although the numerical weather forecasting (NWP) model can predict solar radiation variables, there are often significant errors, especially in direct normal irradiation (DNI), which are influenced by the type and concentration of aerosols and clouds. This paper presents an artificial neural network (ANN) based method to generate operational DNI forecasts using weather and aerosol forecast data from the European Center for medium-range weather forecasts (ECMWF) and Copernicus atmospheric monitoring service (CAMS) respectively. The ANN model is designed to predict weather and aerosol variables at a certain time as input, while other models use the DNI forecast improvement period before the instant forecast. The model was developed using North Sumatra location observations and obtained DNI forecasting results every 10 minutes on the first day with DNI forecasting compared to the initial forecasting which was scaled down with the R2, mean absolute error (MAE), and relative mean square error (RMSE) models were 0.6753, 151.2, and 210.2 W/m2, so that and provides good agreement with experimental data.
Comparative design of harmonic current reduction in variable speed drive using space vector pulse width modulation and hybrid pulse width modulation Siregar, Yulianta; Situmeang, Farel; Mohamed, Nur Nabila
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.pp4907-4920

Abstract

In industry and commerce, three-phase induction motors are frequently utilized as the primary power source for machinery. However, to increase motor performance efficiency, induction motors also need a tool for speed control. The variable speed drive (VSD) is one tool used to control the rotation speed of three-phase induction motors. Since VSD is a non-linear load, harmonic distortion will result from it. The space vector pulse width modulation (SVPWM) injection method and the hybrid pulse width modulation method were the two techniques employed by the author in this study to lower the current in the VSD. With the SVPWM injection approach, the variable speed drive's current total harmonic distortion (THD) values in the R, S, and T phases dropped to 3.77%, 3.53%, and 2.19% from 7.14%, 7.17%, and 7.58%.
Web Block Craft: web development for children using Google Blockly Gunaratne, Madhumini; Weerasekara, Senal; Weerakkody, Dehemi; Sashmitha, Nisal; Zoysa, Rivoni De; Kodagoda, Nuwan
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.pp5585-5592

Abstract

Web Block Craft is an innovative educational application that uses the Google Blockly framework to teach web development to children aged eleven and above. The application serves as a comprehensive learning tool, allowing users to explore both frontend project and backend project development. The frontend project includes HTML, CSS, JavaScript, and DOM manipulation, while the backend project covers server building, web app security, application programming interfaces (APIs), and database management. Web Block Craft's unique block-based interface allows users to easily drag and drop components into a dynamic working environment, resulting in an engaging experience with live output display and simultaneous code presentation. A unique feature of Web Block Craft is the integration of a platform within the application, which allows teachers to create lessons with step-by-step instructions for students. This new feature allows for a more structured learning experience, which improves understanding of web development concepts. To enhance the learning experience, the application provides extensive documentation, serving as a valuable resource for users to grasp the intricacies of web programming. By combining the power of Google Blockly with a creative user interface and educational resources, Web Block Craft provides a comprehensive learning environment that empowers creative web programming with confidence.

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