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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
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Articles 65 Documents
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Search results for , issue "Vol 30, No 3: June 2023" : 65 Documents clear
Model-driven architecture: generating models from Symfony framework M'hamed Rahmouni; Chaymae Talbi; Soumia Ziti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1659-1668

Abstract

The web application development industry is constantly growing due to the extensive use of web applications in different devices, most of them run on Android, iOS, and Windows Phone operating systems. However, the development of applications designed for platforms requires more concerns such as code efficiency, interaction with devices, and speed of market penetration. The model-driven approach (MDA) combined with unified modeling language (UML) could provide abstraction and automation for software developers. This paper presents an MDA approach for the development of web applications based on the Symfony framework, UML modeling, model transformation, and then automatic code generation in order to facilitate and accelerate the development of web applications. The first step of this work is to establish the metamodel of Symfony framework and the metamodel of UML class diagram. In the second step, the various transformation rules between the source and target metamodels are first defined. Atlas transformation language (ATL) implements these rules. The result of this transformation is a platform-specific model (PSM) represented by Ecore language. The generated PSM model represents the input model of model-to-code (M2C) transformation for generating the code of web applications. To validate this work, we have implemented a case study.
Students’ satisfaction with the service quality of academic advising systems Ali M. Ghonmein; Khaldun G. Al-Moghrabi; Tawfiq Alrawashdeh
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1838-1845

Abstract

Academic advising (AA) is an integral part of university education, as it has an indispensable role in helping students fulfil their goals in higher education, become responsible for their own learning, and formulate meaningful educational plans that perfectly match the abilities of each student. For this reason, educational institutions around the world are striving to upgrade their AA systems (AAS) to provide their students with personalized experiences. Meanwhile, modern technology can improve the advising process and facilitate the accomplishment of the corresponding tasks. Therefore, the integration of technology into AA not only offers more flexibility for the students but also improves the delivery of advising services. This study aimed i) to identify those factors that mainly affect the AAS services being offered in universities; and ii) to examine how the satisfaction of students with AAS is affected by service quality. A total of 400 students from the Information Technology College of Al-Hussein Bin Talal University (AHU) were invited to participate in an online survey for data collection, and a response rate of 90.50% was recorded. Results show that the satisfaction of students with the AAS is affected by trust, network quality, service quality, system quality, information quality, and perceived risk.
Improving the efficiency of clustering algorithm for duplicates detection Abdulrazzak Ali; Nurul Akmar Emran; Safiza Suhana Kamal Baharin; Zahriah Othman; Awsan Thabet Salem; Maslita Abd Aziz; Nor Mas Aina Md Bohari; Noraswaliza Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1586-1595

Abstract

Clustering method is a technique used for comparisons reduction between the candidates records in the duplicate detection process. The process of clustering records is affected by the quality of data. The more error-free the data, the more efficient the clustering algorithm, as data errors cause data to be placed in incorrect groups. Window algorithms suffer from the window size. The larger the window, the greater the number of unnecessary comparisons, and the smaller the window size may prevent the detection of duplicates that are supposed to be within the window. In this paper, we propose a data pre-processing method that increases the efficiency of window algorithms in grouping similar records together. In addition, the proposed method also deal s with the window size problem. In the proposed method, high-rank attributes are selected and then preparators are applied to the selected traits. A compensation algorithm is implemented to reduce the problem of missing and distorted sort keys. Two datasets (compact disc database (CDDB) and MusicBrainz) were used to test duplicates detection algorithms. The duplicates detection toolkit(DuDe) was used as a benchmark for the proposed method. Experiments showed that the proposed method achieved a high rate of accuracy in detecting duplicates. In addition, the proposed method.
Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers Diyah Kammel Shary; Habeeb Jaber Nekad; Mazin Abdulelah Alawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1359-1368

Abstract

One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.
Facial recognition for partially occluded faces Omer Abdulhaleem Naser; Sharifah Mumtazah Syed Ahmad; Khairulmizam Samsudin; Marsyita Hanafi; Siti Mariam Binti Shafie; Nor Zamri Zarina
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1846-1855

Abstract

Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50% accuracy for the training set and 95% for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate.
Modeling and automatic generation of data warehouse using model-driven transformation in business intelligence process Redouane Esbai; Soufiane Hakkou; Mohamed Achraf Habri
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1866-1874

Abstract

The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation from the conceptual model (class diagram). Finally, the XML file and SQL code are generated for creating the OLAP cubes and relational data warehouse, respectively. Using transformation languages like query-view-transformation (QVT) and Acceleo, all model transformations are performed.
Emotions recognition from human facial images based on fast learning network Majid Razaq Mohamed Alsemawi; Mohammed Hasan Mutar; Essam Hammodi Ahmed; Hatem Oday Hanoosh; Ali Hashim Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1478-1487

Abstract

Systems of facial emotion recognition have witnessed a high significance in the research field. The face emotions are based on human facial expressions which play a crucial role in silent communication. Machine learning algorithms have widely used in systems of human facial emotion detection from images. However, many systems suffer from low accuracy rate. In this paper, we present a system of facial emotion recognition by using images. In this proposed system, the samples of facial emotions have taken from Yale Face database. In addition, the histograms of oriented gradients (HOG) is used to extract features from the images. The extracted features will feed the fast learning network (FLN) algorithm for the classification part to identify the images of facial emotions with respect to their subjects. Many evaluation measurements have used to evaluate the performance of the proposed system. Based on the results of the experiment, the proposed system achieves 95.04% for the highest accuracy, 72.73% precision. Also, the results of the proposed system in terms of recall, f-measure, and G-main are all equal to 72.73%, respectively.
Image encryption based on combined between linear feedback shift registers and 3D chaotic maps Salah Taha Allawi; Nada Abdul Aziz Mustafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1669-1677

Abstract

Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signal-noise ratio (PSNR), mean square error (MSE) and correlation) were conducted on a group of images to determine the strength and efficiency of the proposed method, and the result proves that the proposed method provided a good level of safety. The obtained results were compared with those of other methods, and the result of comparing confirms the superiority of the proposed method.
Collaborative desing in web aplication development to improve tuberculosis diagnostic Freyre Medrano Juan; Calixto Palacios Carolina; Condori Obregon Patricia; Palomino Vidal Carlos
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1821-1828

Abstract

In Peruvian Tuberculosis Reference Laboratory of the Regional Health Directorate of Callao, issues related to the diagnostic process where identified, because of the slowly generations of patients result. This research develops a web application to solve these issues using a participatory design. Qualitative data were recollected through interviews and focus groups in 45 medical centers belong to the regional health directorate (DIRESA) from Callao. These data were used to define the correct design and develop the required processes in the web application. Quantitative data were recollected either, to measure the efficiency of the new diagnostic process using the web application. The results show that with the use of the web application 120 hours were reduce from the monthly validation results and avoid the generation of 8,700 duplicate information, with these results the diagnostic process was improve. The research also confirms that the design and use of technological tools in a collaborative environment improve the process efficiency.
Estimation of biomass of forage sorghum (sorghum bicolor) Cv. Samurai-2 using support vector regression Kahfi Heryandi Suradiradja; Imas Sukaesih Sitanggang; Luki Abdullah; Irman Hermadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1786-1794

Abstract

One alternative to improve feed quality is to combine the main feed with forages which are more economical in cost but contain high protein sources, such as sorghum. Production estimation is essential because it will determine the sustainability of the feed. This study aimed to estimate the amount of sorghum production using support vector regression (SVR). Several stages of this research are collecting data, preprocessing, modelling, and evaluation. The dataset used and the input for this SVR algorithm model is field observation data. The kernels used in the SVR algorithm modelling are linear, Polynomial, and RBF. Sorghum production estimation using SVR has a performance evaluation value that refers to the root mean square error (RMSE). The result of this research is that the model obtained from the SVR algorithm can estimate sorghum production with performance evaluation values using R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and RMSE. The best results on the Polynomial kernel are R2=0.7841, MAE=0.0681, MAPE=0.46641, and RMSE=0.1006. This study shows that the classification model obtained from the SVR algorithm with Kernel Polynomial is the best model for estimating sorghum production.

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