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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 66 Documents
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Search results for , issue "Vol 24, No 1: October 2021" : 66 Documents clear
Incident forecasting model for motorcycle driving based on IoT and artificial intelligence Esteban Alejandro Cárdenas-Lancheros; Nelson Enrique Vera-Parra
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp444-451

Abstract

Internet of things (IoT) and artificial intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on the analysis of dynamic behavior while driving. The development of this research began with the detection and storage of data associated with the dynamic acceleration variable of a motorcycle while driving, this with the help of a 3-axis accelerometer sensor generating a dataset, which was processed and analyzed for later be taken by three predictive classification models based on machine learning which were decision trees, K-Nearest neighbors and random forests. The performance of each model was evaluated in the task of better classifying the level of accident risk, concerning the driving style based on certain levels of acceleration. The random forest model showed a slightly better performance compared to that shown by the other two models, with 97.24% accuracy and recall, 97.16% precision and 97.17% F1 score.
Ultra low bitrate retinal image compression using integer lifting scheme and subband encoder Yassine Habchi; Ameur Fethi Aimer; Mohammed Beladgham; Riyadh Bouddou
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp295-307

Abstract

Recently, ophthalmic clinics have seen many complaints related to retinal diseases. The degree of clarity of the blood vessels (BV) in the eye can be an important indicator of some diseases affecting the retina such as diabetic retinopathy. To diagnose it, we need to intervene more than a medical team, especially in some difficult cases, through the exchange of medical images obtained by photography. This method has contributed significantly to the production of large data that can quickly saturate transmission, storage systems and increase processing time, so the need to compress images efficiently without modifying the content before transmission represents a major challenge. This paper provides an effective method for compressing color retinal images (CRI), which relies on the use of an integer lifting scheme (ILS) based on cohen daubechies-feaveau wavelet (CDFW9/7) and the set partitioning in hierarchical trees (SPIHT) to encode large coefficients. The obtained results demonstrate that the suggested method reduce algorithmic complexity, improve the retinal image quality and achieves high objective parameters values for ultra-low bitrate compared to the conventional methods.
Convolution neural network and histogram equalization for COVID-19 diagnosis system Bashra Kadhim Oleiwi Chabor Alwawi; Layla H. Abood
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp420-427

Abstract

The coronavirus disease-2019 (COVID-19) is spreading quickly and globally as a pandemic and is the biggest problem facing humanity nowadays. The medical resources have become insufficient in many areas. The importance of the fast diagnosis of the positive cases is increasing to prevent further spread of this pandemic. In this study, the deep learning technology for COVID-19 dataset expansion and detection model is proposed. In the first stage of proposed model, COVID-19 dataset as chest X-ray images were collected and pre-processed, followed by expanding the data using data augmentation, enhancement by image processing and histogram equalization techniuque. While in the second stage of this model, a new convolution neural network (CNN) architecture was built and trained to diagnose the COVID-19 dataset as a COVID-19 (infected) or normal (uninfected) case. Whereas, a graphical user interface (GUI) using with Tkinter was designed for the proposed COVID-19 detection model. Training simulations are carried out online on using Google colaboratory based graphics prossesing unit (GPU). The proposed model has successfully classified COVID-19 with accuracy of the training model is 93.8% for training dataset and 92.1% for validating dataset and reached to the targeted point with minimum epoch’s number to train this model with satisfying results.
Bone cancer detection using electrical impedance tomography Nabanita Saha; Mohammad Anisur Rahaman
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp245-252

Abstract

Bone cancer is an uncommon sort of malignancy that alludes to irregular development of tissue inside the bone, with high opportunity to spread to different pieces of the body. It is extremely important to distinguish bone cancer at the beginning phase to cure it productively. Presently, in addition to a physical examination, magnetic resonance imaging, blood tests, positron emission tomography (PET), computed tomography (CT) or PET-CT scan, X-ray, Bone scan, Biopsy and computed tomography scan, are used to diagnose or determine the stage (or extent) of bone sarcoma. But these methods are costly and not free of radiation. Moreover, these machines are bulky. Electrical impedance tomography approach was proposed in this research for identifying bone cancer as this detection technique is able to distinguish between cancerous and non-cancerous cells by differentiating between their conductivity and it has the possibility to remove the limitations of conventional medical imaging techniques. Here, equivalent bone models were generated using (electrical impedance and diffused optical reconstruction software (EIDORS) which had been implemented in MATLAB, and three different image reconstruction algorithms-GREIT, Sheffield Backprojection, Gauss-Newton inverse algorithm had been used to detect the cancerous cells.
K-affinity propagation clustering algorithm for the classification of part-time workers using the internet Novendri Isra Asriny; Muhammad Muhajir; Devi Andrian
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp464-472

Abstract

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.
Diabetic retinopathy classification using deep convolutional neural network Akshita L.; Harshul Singhal; Ishita Dwivedi; Poonam Ghuli
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp208-216

Abstract

Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence and any seriousness of some subtle features in the fundus images. This process is not very efficient as it takes a lot of time and resources. DR testing of all the patients, a lot of which are undiagnosed or untreated, is a big task due to the inefficiency of the traditional method. This paper was written with the aim to propose a classification system based on an efficient deep convolution neural network (DCNN) model which is computationally efficient. Amongst other supervised algorithms involved, proposed solution is to find a way to efficiently classify the fundus images into 5 different levels of severity. Application of segmentation after the pre-processing and then use of deep convolutional neural networks on the dataset results in a high accuracy of 91.52%. The result achieved is high given the limitations of the dataset and computational powers.
Detailed modelling and simulation of single-phase transformers for research and educational purposes Saif Sabah Sami; Mazin T. Muhssin; Zeyad Assi Obaid; Ali N. Hussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp37-49

Abstract

COVID-19 pandemic, despite its devastating impact, accelerated the shift to e-learning in higher education. Particularly in the electrical machines courses, that often include laboratory experiments. However, no detailed models of transformers, developed in Simulink/MATLAB®, were reported in the literature. Hence, in this paper, a virtual laboratory consists of models of single-phase transformers was built for the first time. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for students to replicate easily to support and enhance the learning process of electrical machines courses. Consequently, the developed models are effective tools for educational and research purposes. Dynamic models of single-phase, two-winding, transformers and step-up and step-down auto-transformers were developed using Simulink/MATLAB®. Two different approaches for modelling were proposed, the block diagram representation and Simscape based models. The two modelling methods were validated against the built-in transformer model. The developed models have been successfully integrated into electrical engineering courses at Middle Technical University, Baghdad, Iraq. Therefore, all developed models are freely available online at a dedicated repository.
MATLAB based design and performance analysis of electronically commutated BLDC motor Issa Etier; Anci Manon Mary A.; Nithiyananthan Kannan
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp22-28

Abstract

The main objective of this research work is to design the electronically communtated brushless direct current (BLDC) motor and analysis its performance in MATLAB environment. The use of BLDC engine is expanding daily, the performance analysis is progressively significant and the consumer loyalty is significant. In light of the ranking and requirements, the BLDC engine is planned. The BLDC motor is widely used in a variety of fields. Low ripple input supply and a suitable speed controller are needed to achieve desired motor output. The output of BLDC motors, such as torque, voltage, and speed response, is examined in this paper. The controller parameters have been fine-tuned to improve motor speed. It has been discovered that a three phase voltage source inverter (VSI) fed BLDC motor with a fractional-order proportional-integral-derivative (FOPID) controller provides superior BLDC motor response. The outcomes are broke down utilizing the MATLAB programming.
Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation John Joshua Federis Montañez
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp90-98

Abstract

Standard laboratory soil testing is deemed to be expensive and time-consuming. Utilizing a soil test kit is considered to be a cost-efficient and time-saving way of soil testing. This project study aims to develop a prototype that detects soil parameters (i.e., soil pH, nitrogen, phosphorus, and potassium) and gives crop and fertilizer recommendations after the soil sample has undergone a soil treatment test kit and its acceptability for possible users. The prototype development primarily used image processing to detect the needed parameters that lead to crop and fertilizer recommendations. In the evaluation of the effectiveness of the prototype, 50 trials were conducted per parameter. All of the said parameters were recorded as highly effective except for nitrogen Low, which is interpreted as effective only. There were 30 possible users invited to assess the acceptability of the prototype. A survey based on the technology acceptance model was administered to the 30 respondents garnering a 4.85 weighted mean interpreted as excellent. The prototype was proven effective and accepted as a device that can detect soil pH and primary macronutrient levels. It gives the appropriate crop and fertilizer recommendations based on the gathered data.
The absorber and buffer layer thicknesses for CdTe/CdS based thin film solar cell efficiency at various operational temperatures Omar Ghanim Ghazal; Ahmed Waleed Kasim; Nabeel Zuhair Tawfeeq
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp70-80

Abstract

Cadmium telluride (CdTe)/cadmium sulfide (CdS) solar cell is a promising candidate for photovoltaic (PV) energy production, as fabrication costs are compared by silicon wafers. We include an analysis of CdTe/CdS solar cells while optimizing structural parameters. Solar cell capacitance simulator (SCAPS)-1D 3.3 software is used to analyze and develop energy-efficient. The impact of operating thermal efficiency on solar cells is highlighted in this article to explore the temperature dependence. PV parameters were calculated in the different absorber, buffer, and window layer thicknesses (CdTe, CdS, and SnO2). The effect of the thicknesses of the layers, and the fundamental characteristics of open-circuit voltage, fill factor, short circuit current, and solar energy conversion efficiency were studied. The results showed the thickness of the absorber and buffer layers could be optimized. The temperature had a major impact on the CdTe/CdS solar cells as well. The optimized solar cell has an efficiency performance of >14% when exposed to the AM1.5 G spectrum. CdTe 3000 nm, CdS 50 nm, SnO2 500 nm, and (at) T 300k were the I-V characteristics gave the best conversion open circuit voltage (Voc)=0.8317 volts, short circuit current density (Jsc)=23.15 mA/cm2, fill factor (FF)%=77.48, and efficiency (η)%=14.73. The results can be used to provide important guidance for future work on multi-junction solar cell design.

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