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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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Analysis study of quality factor and bit error rate at wavelength change Fadhela Thaeer Mahmood; Alaa H. Ali; Alaa H. Ali Haeder
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp301-308

Abstract

Gigabit - per - second data speeds can be achieved via free space optics (FSO) lines, which needless system complexity. On the other hand, the link's availability across a wide range of atmospheric circumstances is a major worry. As a result of the increased signal attenuation caused by the linkages being weather - dependent, their efficiency decreases. Up to 70 dB/km of attenuation can be caused by bad weather on a 500 - meter free space optics link. In this work, transmission windows of 1310 nm, 850 nm , and 1550 nm are analyzed and compared using the free space optics link. by using the Simulation program o pti system such as the q uality factor, the minimal bit error rate (BER), and the e ye diagram is taken into account. Analyzer findings are compared to establish the optimal wavelength for a transmitter under poor weather conditions.
High-efficiency green and red phosphors enable a broader hue-gamut light-emitting diode backlight for brighter displays Dieu An Nguyen Thi; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp741-747

Abstract

In this article, we suggest combining a blue InGaN chip with strait-band green (β-sialon:Eu2+), red (K2SiF6:Mn4+) phosphors to create WLED devices with a wide hue range and high effectiveness that may be utilized in LCD backlighting. The highest radiation wavelength of a gas-pressure sintedβ-sialon:Eu2+ is 535 nm, the full width at half maximum (FWHM) is 54 nm, and the outside quantum performance is 54.0% lower than the 450 nm stimulation. We created K2SiF6:Mn4+ in two steps. The phosphor possesses a sharp line radiation spectrum accompanied by the most intense maximum point under 631 nm, an FWHM reaching roughly 3 nm, as well as an exterior quantum effectiveness of 54.5%. When computed at 120 mA, the manufactured three-range wLEDs had an illuminating performance of 91–96 lm/W and a large color temperature of 11,184–13,769 K (i.e., 7,828–8,611 K in LCD screens). The hue range represented by the CIE 1931 and CIE 1976 hue gaps is 85.5-85.9% and 94.3-96.2% of the NTSC requirement, respectively.  The optic characteristics outperform those of phosphor-transformed wLED backlights utilizing broad-range green or red phosphors, indicating the two strait-range phosphors studied are the best luminous substances for producing brighter and livelier screens.
Prediction of student satisfaction on mobile-learning by using fast learning network Laman Radi Sultan; Salwa Khalid Abdulateef; Bushra Abdullah Shtyat
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp488-495

Abstract

The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.
Internet of things based wireless sensor network: a review Shayma Wail Nourildean; Mustafa Dhia Hassib; Yousra Abd Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp246-261

Abstract

Recently, Internet of Things (IoT) technologies are developing technology with a variety of applications. The Internet of Things (IoTs) is defined as a network of ordinary objects such as Internet TVs, smartphones, actuators and sensors that are smartly connected together to enable new types of communication between people and things as well as between things themselves. Wireless sensor networks (WSNs) play an important part in Internet of Things (IoT) technology. A contribution to wireless sensor networks and IoT applications is wireless sensor nodes’ construction with high-speed CPUs and low-power radio links. The IoT-based wireless Sensor network (WSN) is a game-changing smart monitoring solution. ZigBee standard is an important wireless sensor network (WSN) and Internet of Things (IoT) communication protocol in order to facilitate low-power, low-cost IoT applications and to handle numerous network topologies. This paper presented a review on the energy efficient and routing topologies of ZigBee WSN, applications of IoT enabled Wireless Sensor Network as well IoT WSN security challenges.
Blockchain adoption barriers in Moroccan sustainable supply chain: a proposed approach Abdesadik Bendarag; Omar Boutkhoum; Driss Abada; Mohamed Hanine
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp892-899

Abstract

Currently, the emerging countries like Morocco seeks to benefit from the potential of blockchain technology to meet its various growing demands, especially in sustainable supply chain management (SSCM). This explains the need for more effort to understand blockchain implementation and identify the barriers influencing the blockchain adoption decision in SSCM, especially, from Moroccan industry and service sectors perspective. In this context, this research paper proposes a group decision-making approach to identify the barriers from a comprehensive literature search, then evaluate them based on intuitionistic fuzzy analytic hierarchy process (IFAHP). Due to the varied importance of the selected barriers, IFAHP is utilized to allocate priority weights for each barrier according to its importance level. The evaluation results reveal that “Government policy and support” and “Challenges in integrating sustainable practices and blockchain technology through sustainable supply chain management (SCM)” are the best ranked barriers that impact the implementation of blockchain technology in Moroccan context. The main objective is to inquire the barriers preventing the blockchain implementation, and assist industry decision-makers in developing supple short- and long-term decision-making strategies for better sustainable supply chain management.
Breast cancer recognition based on performance evaluation of machine learning algorithms Chiman Haydar Salih; Abbas M. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp980-989

Abstract

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.
A semi-automated hybrid approach to identify radicalization on social digital platform Vandna Batra; Suresh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp563-572

Abstract

The digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment.
Early disease prediction algorithm for hypertension-based diseases using data aware algorithms Yasmeen Shaikh; Vasudev Parvati; Sangappa Ramachandra Biradar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1100-1108

Abstract

This paper implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.
Design and modeling of solar water pumping system in Diyala region Mohammed Hasan Ali; Raghad Ali Mejeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp79-85

Abstract

In recent years, solar panels have become increasingly popular for converting solar energy is converted into electrical energy. The solar panel can be utilized as part of a larger solar system that is connected to the power grid or as a stand-alone system. Every day, the world receives 84 Terawatts of energy, yet we only use about 12 Terawatts. For optimal energy conversion, the tracking mechanism will keep the solar panel perpendicular to the sun at all times. In this setup, photo resistors will be employed as sensors. A light detection system, a microprocessor, a gear motor system, and a solar panel will make up the system. When compared to solar panels without tracking equipment, our system will produce up to 40% more electricity. Improvements to the board's efficiency include the addition of a dust sensor. The dust on the board is also detected by the sensor, which activates a pump inside the tank. It uses the Arduino to pump water onto the board to clean it of dust and maintain its efficiency. There is also a water sensor. When the tank's water level falls below a certain level, the attached pump activates.
The luminescence efficiency of green phosphor Ca7(PO4)2(SiO4)2:Eu2+ for white light-emitting diode Van Liem Bui; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp773-779

Abstract

This study examines the green-emission phosphor composition of Eu2+ doped Ca7(PO4)2(SiO4)2 to serve the goal of efficiency enhancement for the white light emitting diode (LED). The process of preparation and photoluminescent investigation of proposed phosphor composition was monitored under near UV excitation wavelength of the LED die. The sample phase was determined using XRD. To explore Ca7(PO4)2(SiO4)2:Eu2+ capabilities, the diffuse reflectance and photoluminescence spectral figures were employed. The ultraviolet absorption of Ca7(PO4)2(SiO4)2:Eu2+ranged from 240 to 440 nm, with a wide band of green emission peaking at 522 nm. Besides the concentration quenching mechanism, we also focus on essential characteristics for white-light-emitting diode(WLED) production like temperature-dependent lumen output and chromaticity coordinates.

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