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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 :
Arjuna Subject : -
Articles 9,174 Documents
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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.
Formalization of risk management in the context of digital business transformation Koshekov Kairat; Alibekkyzy Karlygash; Toiganbayev Beglan; Belginova Saule; Keribayeva Talshyn; Tulaev Viktor; Koshekov Abai
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.pp1428-1439

Abstract

The aim of the article is to develop a formal methodology for quantitative assessment of the quality of аcontrol in a closed system with feedback in the context of digital transformation. In the proposed study, attention is focused on assessing the quality of management in organizational and technical systems on the example of the aviation industry. The following hypotheses were adopted in the study: in the digital management of business processes of an economic entity, the role of intellectual support is acquired by methods of formal description of processes: control, decision-making and corrective action on the control object. In critical situations, the psychotype of the person making the decision acquires a decisive role. The study solves two scientific and practical problems: development of a formal method for quantitative assessment of the quality of management of a complex multi-criteria organizational and technical system under the conditions of statistical uncertainty of management agents, taking into account feedback in the management of an object; formalization of the process of quantitative assessment of decision-making risks in the environment of statistical uncertainty of control agents and psychological factors of the decision maker.
Machine learning prediction of petty corruption intention among law enforcement officers Suraya Masrom; Rahayu Abdul Rahman; Nor Asyiqin Salleh; Endang Pitaloka; Mohd Auzan Md Nor; Nor Balkish Zakaria
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.pp1634-1642

Abstract

Law enforcement agencies face a widespread problem of corruption, which jeopardizes their credibility and institutional integrity. Thus, the primary goal of this study is to develop a machine learning prediction model for petty corruption intentions as an early warning system for law enforcement officials who fail to perform their duties and obligations with integrity. Using a questionnaire survey of two hundred twenty-five participants, from senior officers to rank and file police officers, this study presents the fundamental knowledge on the design and implementation of machine learning model based on six selected algorithms; generalized linear model, fast last margin, decision tree, random forest, gradient boosted trees, and support vector machine. In addition to demographic factors, the efficacy of each machine learning algorithm on petty corruption was evaluated using general strain theory (GST) attributes: financial stress, work stress, leadership pressure, and peer pressure. The findings indicated that peer pressure has given the highest weight of contributions to most of the machine learning algorithms. The most outperformed machine learning in terms of the classification accuracy is gradient boosted trees with accuracy above 90%. This paper presents useful knowledge to enhance the realization of implementing intelligent corruption detection tools.
Comparing performance of bastion host on cloud using Amazon web services vs terraform Sahana Bailuguttu; Akshatha S. Chavan; Oorja Pal; Kavya Sannakavalappa; Dipto Chakrabarti
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.pp1722-1728

Abstract

In addition to security advantages like implementing defense in depth and complying with compliance standards, current bastion services are simple to deploy and fit into the DevOps culture. Bastions continue to be the most dependable and secure options for secure access to cloud infrastructures because they offer administrative simplicity without surrendering compliance and security. In this paper, an experimental set up was conducted to measure the cycle time it takes to provision resources using manual point-and-click graphical user interface (GUI) in Amazon web services (AWS) and time it takes for codified infrastructure to make application programming interface (API) calls using terraform. It also focuses on the design and deployment of Bastion host on AWS and terraform, and the comparison between the two with respect to various parameters.

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