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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 65 Documents
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Search results for , issue "Vol 25, No 1: January 2022" : 65 Documents clear
Systematic literature review on university website quality Ala' Hasan Saleh; Rasimah Che Mohd Yusoff; Nur Azaliah Abu Bakar; Roslina Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp511-520

Abstract

Website is a necessity for organizations to enable users worldwide to access their information and gain a competitive edge over others. The diversity of websites makes assessing website quality a difficult task. The aim of this paper is to identify the issues faced in the quality evaluation of university websites, the models and the factors used for evaluating university website quality. Systematic literature review was used to identify and synthesize related scholarly research papers. Findings show that there is a lack of study on university website quality compared to business websites; website designers did not have the appropriate knowledge on the interface design; and the website quality evaluation is complex since there is no specific evaluation model. Webqual 4.0 model was used to evaluate the quality of universities' websites. From 24 studies, initially 79 quality factors were extracted. After performing comparison, filtration and memoing, six quality factors were identified: information quality, specific content, usability, web appearance, service interaction quality, and functionality. This study makes a useful contribution in developing university website quality model by extending the Webqual 4.0 model.
A deep learning approach based on stochastic gradient descent and least absolute shrinkage and selection operator for identifying diabetic retinopathy Thirumalaimuthu Thirumalaiappan Ramanathan; Md. Jakir Hossen; Md. Shohel Sayeed; Joseph Emerson Raja
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp589-600

Abstract

More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Reference-free differential histogram-correlative detection of steganography: performance analysis Natiq M. Abdali; Zahir M. Hussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp329-338

Abstract

Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless  modified.
Agricultural harvesting using integrated robot system Vikram Raja; Bindu Bhaskaran; Koushik Karan Geetha Nagaraj; Jai Gowtham Sampathkumar; Shri Ram Senthilkumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp152-158

Abstract

In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.
Data transmitted encryption for clustering protocol in heterogeneous wireless sensor networks Basim Abood; Abeer Naser Faisal; Qasim Abduljabbar Hamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp347-357

Abstract

In this paper, elliptic curves Diffie Hellman-Rivest Shamir Adleman algorithm (ECDH-RSA) is a novel encryption method was proposed, which based on ECDH and RSA algorithm to secure transmitted data in heterogeneous wireless sensor networks (HWSNs). The proposed encryption is built under cheesboard clustering routing method (CCRM). The CCRM used to regulate energy consumption of the nodes. To achieve good scalability and performance by using limited powerful max-end sensors besides a large powerful of min-end sensors. ECDH is used for the sharing of public and private keys because of its ability to provide small key size high protection. The proposed authentication key is generated by merging it with the reference number of the node, and distance to its cluster head (CH). Decreasing the energy intake of CHs, RSA encryption allows CH to compile the tha data which encrypted with no need to decrypt it. The results of the simulation show that the approach could maximize the life of the network by nearly (47%, and 35.7%) compare by secure low-energy adaptive clustering hierarchy (Sec-LEACH and SL-LEACH) approches respectively.
Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique Virupaxi Balachandra Dalal; Satish S. Bhairannawar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp291-297

Abstract

Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more effectively.
Efficiency of hybrid algorithm for COVID-19 online screening test based on its symptoms Mohd Kamir Yusof; Wan Mohd Amir Fazamin Wan Hamzah; Nur Shuhada Md Rusli
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp440-449

Abstract

The coronavirus COVID-19 is affecting 196 countries and territories around the world. The number of deaths keep on increasing each day because of COVID-19. According to World Health Organization (WHO), infected COVID-19 is slightly increasing day by day and now reach to 570,000. WHO is prefer to conduct a screening COVID-19 test via online system. A suitable approach especially in string matching based on symptoms is required to produce fast and accurate result during retrieving process. Currently, four latest approaches in string matching have been implemented in string matching; characters-based algorithm, hashing algorithm, suffix automation algorithm and hybrid algorithm. Meanwhile, extensible markup language (XML), JavaScript object notation (JSON), asynchronous JavaScript XML (AJAX) and JQuery tehnology has been used widelfy for data transmission, data storage and data retrieval. This paper proposes a combination of algorithm among hybrid, JSON and JQuery in order to produce a fast and accurate results during COVID-19 screening process. A few experiments have been by comparison performance in term of execution time and memory usage using five different collections of datasets. Based on the experiments, the results show hybrid produce better performance compared to JSON and JQuery. Online screening COVID-19 is hopefully can reduce the number of effected and deaths because of COVID.
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop; Siti Fatimah Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp59-67

Abstract

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.
Comparative study of low power wide area network based on internet of things for smart city deployment in Bandung city Muhammad Imam Nashiruddin; Maruli Tua Baja Sihotang; Muhammad Ary Murti
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp425-439

Abstract

Smart city implementation, such as smart energy and utilities, smart mobility & transportation, smart environment, and smart living in urban areas is expanding rapidly worldwide. However, one of the biggest challenges that need to be solved is the selection of the appropriate internet of things (IoT) connectivity technologies. This research will seek for the best candidate low power wide area network (LPWAN) technologies such as long-range wide area network (LoRaWAN), narrow-band internet of things (NB-IoT), and random phase multiple access (RPMA) for IoT smart city deployment in Bandung city is based on IoT network connectivity between with six technical evaluation criteria: gateway requirements, traffic/data projection, the best signal level area distribution, and overlapping zones. Bass model is carried out to determine the capacity forecast. While in coverage prediction, LoRaWAN and NB-IoT use the Okumura-Hata propagation, and Erceg-Greenstein (SUI) model is used for RPMA. Based on the simulation and performance evaluation results, RPMA outperforms LoRaWAN and NB-IoT. It required the least gateway number to cover Bandung city with the best signal levels and overlapping zones.
K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment Omar Freddy Chamorro-Atalaya; Guillermo Morales Romero; Adrián Quispe Andía; Beatriz Caycho Salas; Elizabeth Katerin Auqui Ramos; Primitiva Ramos Salazar; Carlos Palacios Huaraca
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp521-528

Abstract

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.

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