cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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
 687   688   689   690   691 
Study of phosphor Ba2Si3O8:Eu2+ to produce WLED devices with support from ZnCdSe/ZnSe quantum dot Huu Phuc Dang; Bui Van Hien; Nguyen Le Thai
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp729-734

Abstract

We created the blue-green Ba2Si3O8:Eu2+ (or BaE) phosphor treated with Eu2+ using the standard solid-state method with the concentration of Eu as well as heating temperature properly adjusted for the maximum luminescence efficacy. It is possible to excite the said phosphor using near-UV (n-UV) wavelengths and to display its wide emission band, which is the 5d => 4f shift for Eu2+, caused by the combination of the Eu activator and the nearby host. We integrated the said phosphor with the n-UV LED to create the pc-LED (short for diodes based on conversion phosphor). For the task of creating the WLED device that yields significant color rendering index, we combined the orange ZnCdSe/ZnSe quantum dot with a distinctive sheet structure for the pc-LED made with phosphor BaE. This research demonstrates the electroluminescence features of the said elements.
Design of an orthopedic smart splint using nickel-titanium shape memory alloy Azza Alhialy; Warqaa H. Alkhaled; Tahani G. Al-Sultan; Zaid H. Al-Sawaff; Fatma Kandimerli
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1300-1309

Abstract

People with broken bones suffer from symptoms of muscular atrophy as a result of a lack of movement, so it was necessary to find effective solutions due to the relative pain they cause and the difficulty of movement after healing. In this paper, we proposed a smart splint made of nickel-titanium shape memory alloys (SMA) wires. These alloys have unique properties compared to other materials, the most important of which is maintaining the original shape during manufacturing at a certain temperature. Temperature, pressure, as well as humidity, were analyzed and monitored while the patient wore the splint to reach the best possible results by using a microcontroller. The results showed that there was a significant improvement for the muscles in a short time when using the proposed splint, as the percentage of qualified muscle recovery increased by more than 70% when using the usual splint. The wires used had an effective role in rehabilitating these muscles by performing a permanent local massage. due to the different diameters of these wires, a different response to temperature change was recorded.
Support system of self-assessment and gap analysis for new normal tourism standards Soawanee Prachayagringkai; Marut Buranarach; Pongpisit Wuttidittachotti
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp384-395

Abstract

Tourism after the outbreak of the emerging epidemic of COVID-19 has drastically changed. Tourist attractions will be certified with Green National Park and New Normal Standards. Starting in the year 2021 onwards, Thailand's national parks are important tourist destinations, of which 155 nationwide will be subject to complying with such standards to ensure safety, hygiene and environmentally friendly service starting in the year 2021 onwards. This research aims to develop a support system for self-assessment and gap analysis based on Smart Self-Assessment for New Normal Tourism Standards to enable the national parks to assess themselves and be prepared for future actual assessments. The system development focuses on user data import design and report output, system performance test, self-assessment score percentage difference tests, and system performance evaluation by the experts. The percentage difference of self-assessment scores is found at 0.0 for all items after adding details in some of the work lists based on the experts’ opinions, whereas, the performance testing indicates that the system developed is applicable and highly efficient (= 4.40, S.D.= 0.54).
A hybrid bat-genetic algorithm for improving the visual quality of medical images Kholood N. Hussin; Ali K. Nahar; Hussain Kareem Khleaf
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp220-226

Abstract

Efficient repression of noise in a medical image is a very significant issue. This paper proposed a method to denoise medical images by the use of a hybrid adaptive algorithm based on the bat algorithm (BA) and genetic algorithm (GA). Medical images can be often affected by different kinds of noise that decrease the precision of any automatic system for analysis. Therefore, the noise reduction methods are always utilized for increasing the Peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) of images to optimize the originality. Gaussian noise and salt and pepper noise corrupted the used medical data, separately. The noise level to medical images was added noise variance from 0.1 to 0.5 to compare the performance of the de-noising techniques. In the analytical study, we apply different kinds of noise like Gaussian noise and salt-and-pepper noise to medical images for making these images noisy. The hybrid BA-GA model was applied on medical noisy images to eliminate noise and the performances have been determined by the statistical analyses such as PSNR, values are gotten 63.04 dB and 59.75 dB for CT and MRI images.
Feature based analysis of endometriosis using machine learning Visalaxi Sankaravadivel; Sudalaimuthu Thalavaipillai; Surya Rajeswar; Pon Ramlingam
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1700-1707

Abstract

Machine learning is a cutting-edge technology used for predicting and diagnosing various diseases. Various machine learning algorithm facilitates the prediction. The decision tree belongs to learning algorithm that performs both classification and prediction. The decision tree constructs the tree-like to evaluate the best features. The decision tree performs well in the prediction of various diseases. Endometriosis is a recurrence disease that creates an emotional impact in women. Endometriosis is a lump-like structure that appears at several locations in reproductive organs of women. The diagnosis of endometriosis was predicted through scanning procedures and laparoscopic procedures. The symptoms identified from laparoscopic surgery were used as the features for predicting the severity of endometriosis. The symptoms include mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes. The decision tree analyze the features of endometriosis by using two criteria such as entropy and Gini index. The entropy and Gini index construct the tree by identifying the size of tissue as major influencing attributes. The Gini index outperforms well with training accuracy of 84.08% and test accuracy of 84.85.
Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter Shiju Thomas; Addapalli Krishna
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp777-786

Abstract

Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio and structural similarity index metrics are determined to prove the performance of the proposed method. The evaluated results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the DICOM image for diagnosis purposes.
Improving optical properties of white light-emitting diodes using triple-layer remote structure Thanh Binh Ly; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1242-1250

Abstract

Inrecent efforts to improve the performance ofwhite light-emitting diode (WLEDs), researchers have focused on angular color uniformity (ACU), an effective index for evaluating the light quality of WLEDs. Inthis article, we also aim for the WLED development by applying three phosphor layers in the remote phosphor structure. The dual-layer phosphor (DLP) remote structure is also included in the research for comparison with the triple-layer phosphor (TLP) in terms of their impacts on the lighting quality of WLEDs. To ensure the diversity and applicability in different scenarios, performancesof multi-layer phosphor structures in WLED devices with average correlatedcolor temperatures (ACCTs) from 5600 K to 8500K are measured. The experimental results have proved that both TLP and DLP structures are suitable to enhance WLEDs’ performance as each structure excels at specific qualities. In particular, at all ACCTs, the DLP structure is getting better in improvingthe color rendering index (CRI), while the TLP is more advantageous to color quality scale (CQS) and light output. The TLP also presents a lower color deviation than the DLP does, which leads to a better color uniformity in WLEDs at all ACCTs.
Machine learning classification-based portscan attacks detection using decision table Mahdi Nsaif Jasim; Ali Munther Abdul Rahman; Muthanna Jabbar Abdulredhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1466-1472

Abstract

Port scanner attackers are typically used to identify weak points or vulnerabilities in an organization's network. When attackers send a detective message to a port number, the response tells them whether the port is open and assists them in identifying potential vulnerabilities. However, machinelearning approaches are the most effective techniques for detecting and identifying port scanner attacks. This attack is regarded as one of the most dangerous internet threats. This research aims to strengthen the detection accuracy and reduce the detection time. Tagged network traffic data sets are used to train the classification machine learning techniques. On the other hand, network traffic analysis is used by unsupervised method to detect attacks. This study modifies the decision table and OneR classification algorithms as a supervised technique for portscan detection. The proposed algorithm uses the CICIDS2017 dataset for both training and testing. The proposed hybrid feature selection methods use and apply multiple training and testing through a sequence of experiments, the proposed method is capable of detecting the portscan attack with 99.8% accuracy, which is competitive in addition to the proposed combination's fast response.
Increasing validation accuracy of a face mask detection by new deep learning model-based classification Mohanad Azeez Joodi; Muna Hadi Saleh; Dheyaa Jasim Kadhim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp304-314

Abstract

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieved lower computational complexity and number of layers, while being more reliable compared with other algorithms applied to recognize face masks. The findings reveal that the model's validation accuracy reaches 97.55% to 98.43% at different learning rates and different values of features vector in the dense layer, which represents a neural network layer that is connected deeply of the CNN proposed model training. Finally, the suggested model enhances recognition performance parameters such as precision, recall, and area under the curve (AUC).
An overview of number theory research unit variant development security Saba Alaa Abdulwahhab; Qasim Mohammed Hussein; Imad Fakhri Al-Shaikhli
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1164-1173

Abstract

Number theory research unit (NTRU) become the most important of securi-ty in recent, with its modification of their variant, this paper search of the literature and A number of studies have examined the in public key variant development and security. In general, prior work is limited to a subset of public key increasing complexity but the benefits of speed up encryption/ decryption have not been fully established. So this paper will be the basis for those who want to develop and find proposed solutions for new studies of the NTRU algorithm. This paper aims to develop a framework to investigate the NTRU development, had been discovered that despite its development over the years and even its acceptance in round three of post quantum cryptograph, then found that limit study in the new scope of quantum facility and the ability of hybrid of new study.

Page 689 of 918 | Total Record : 9174

 687   688   689   690   691 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue