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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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Application of the Jaya algorithm to solve the optimal reliability allocation for reduction oxygen supply system of a spacecraft Saad Abbas Abed; Mohammad Aljanabi; Noor Hayder Abdul Ameer; Mohd Arfian Ismail; Shahreen Kasim; Rohayanti Hassan; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1202-1211

Abstract

In this paper the reliability of reduction oxygen supply system (ROSS) of a spacecraft which was calculated as a complex system using minimal cut method. The reliability of each component of system was calculated as well as the reliability importance of the system. The cost of each component of the system was possible approaches of the allocation values of reliability based the minimization of the overall cost in this system. The advantage of this algorithm can be used to allocate the optimization of reliability for simple or complex system. This optimization is achieved using the Jaya algorithm. The proposed technique is based on the notion that a conclusion reached on a particular problem should pass near the best results and avoid the worst outcomes. The original findings of this paper are: i) the system used in this paper is a spacecraft’s reduced oxygen supply system with the logarithmic cost function; and ii) the results obtained were by using the Jaya algorithm to solve specific system reliability optimization problems.
Evaluation of one of leading Indonesia’s digital wallet using the unified theory of acceptance and use of technology Natalia Limantara; Jevan Jovandy; Anindya Khansalihara Wardhana; Steven Steven; Fredy Jingga
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1036-1046

Abstract

The goal of this research is to assess the utilization of one of Indonesia's most popular digital wallets. Respondents in this study range in age from 15 to 44 years old and live on Java Island. The author employs the unified theory of acceptance and utilization of technology (UTAUT) paradigm to assess the use of this digital wallet. This UTAUT model comprises four latent variables that affect behavioral intention (BI) and use behavior (UB): performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In this study, the UTAUT model is combined with two additional variables: perceived risk (PR) and promotional benefits (PB). According to the findings, performance expectancy, social influence, and perceived risk all have an impact on behavioral intention, whereas effort expectancy has a less impact. Furthermore, the factors facilitating conditions and promotional benefits have a minor impact on use behavior, whereas behavioral intention factors have a considerable impact on use behavior.
A semantic web services discovery approach integrating multiple similarity measures and k-means clustering Mourad Fariss; Naoufal El Allali; Hakima Asaidi; Mohamed Bellouki
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1228-1237

Abstract

Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.
A simple, effective distance and density based outlier detection algorithm Sajidha S. A.; Udai Agarwal; Pruthviraj R. P.; Sparsh Agarwal; Nisha V. M.; Amit Kumar Tyagi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1141-1148

Abstract

Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.
Intrusion detection system based on bagging with support vector machine Ali Khalid Hilool; Soukaena H. Hashem; Shatha H. Jafer
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1100-1106

Abstract

Due to their rapid spread, computer worms perform harmful tasks in networks, posing a security risk; however, existing worm detection algorithms continue to struggle to achieve good performance and the reasons for that are: First, a large amount of irrelevant data affects classification accuracy. Second, individual classifiers do not detect all types of worms effectively. Third, many systems are based on outdated data, making them unsuitable for new worm species. The goal of the study is to use data mining algorithms to detect worms in the network because they have a high ability to detect new types accurately. The proposal is based on the UNSW NB15 dataset and uses a support vector machine to train and test the ensemble bagging algorithm. To detect various types of worms efficiently, the contribution suggests combining correlation and Chi2 feature selection method called Chi2-Corr to select relevant features and using support vector machine (SVM) in the bagging algorithm. The system achieved accuracy reaching 0.998 with Chi2-Corr, and 0.989, 0.992 with correlation and chi-square separately.
IMUW-APP: An instrument for measuring the usability of web applications Ayad Hameed Mousa; Mowafak K. Mohsen; Ali M. Alnasrawi; Intedhar Shakir Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1183-1194

Abstract

Conventional usability measurement methods for measuring web applications are costly, sometimes time-consuming, and may require professionals. The frameworks, methods, approaches, and tools in which web applications are designed can fully support these limitations. The main issue is to speed up the evaluation process of websites in an effortless manner. To overcome this limitation, this paper proposes an instrument that can use for measuring the usability of web applications (IMUW-APP). A systematic literature review was utilized to determine the instrument dimensions and their items. The validity and reliability test were conducted via face and content validity, goodness testing, and pilot study. Cronbach's Alpha, factor loading, Kaiser-Meyer-Olkin, and Barlett's test were +calculated to ensure the validity and reliability of the proposed instrument. In the light of our analyses, the obtained findings indicate that the proposed instrument (IMUWAPP) is workable and can adapt. Besides, a case study is used to verify the proposed instrument to evaluate a university website. The collecting data have been analyzed and visualized. Ultimately, the overall findings have highlighted.
Electricity consumption forecasting using DFT decomposition based hybrid ARIMA-DLSTM model Osman Yakubu; Narendra Babu C.
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1107-1120

Abstract

Forecasting electricity consumption is vital, it guides policy makers and electricity distribution companies in formulating policies to manage production and curb pilfering. Accurately forecasting electricity consumption is a challenging task. Relying on a single model to forecast electricity consumption data which comprises both linear and nonlinear components produces inaccurate results. In this paper, a hybrid model using autoregressive integrated moving average (ARIMA) and deep long short-term memory (DLSTM) model based on discrete fourier transform (DFT) decomposition is presented. Aided by its superior decomposition capability, filtering using DFT can efficiently decompose the data into linear and nonlinear components. ARIMA is employed to model the linear component, while DLSTM is applied on the nonlinear component; the two predictions are then combined to obtain the final predicted consumption. The proposed techniques are applied on the household electricity consumption data of France to obtain forecasts for one day, one week and ten days ahead consumption. The results reveal that the proposed model outperforms other benchmark models considered in this investigation as it attained lower error values. The proposed model could accurately decompose time series data without exhibiting a performance degradation, thereby enhancing prediction accuracy.
New algorithm for clustering unlabeled big data Marwan B. Mohammed; Wafaa AL-Hameed
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1054-1062

Abstract

The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.
Balanced islanding detection of integrated DG with phase angle between voltage and current M Krishna Goriparthy; B Geetha Lakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp32-40

Abstract

The integration of renewable energy systems is enhancing in daily life for supplying the global demand for electric energy. The concerning problem with the integration of such distributed generation (DG) is islanding. It may damage the consumers and equipment. As per the IEEE 1547 DG integration specifications it must be identified in 2 seconds. In this article a novel passive recognition approach occupying on the rate of change of phase angle between positive sequence voltage and current is (RCPABPSVAC) is proposed. The existing passive methods failed to detect balanced and low power mismatch islanding cases. The suggested approach can do it and strongly classifies the non islanding situations with the islanding situations. The simulations are implemented in MATLAB/Simulink environment.
Experimental study of through the wall imaging for the detection of vital life signs using SFWR Pardhu Thottempudi; Vijay Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp825-830

Abstract

Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wallimaging techniques are designed but didn’t identify the vital signs behind walls with accurate working. Therefore, a novel advance wall image tracking method is required identification of human target. An experimental study on through the wallimaging (TWI) to detect the life signs using sweep frequency continuous wave radar (SFCWR) is explained in this paper. The proposed system consists of agilent vector network analyzer (VNA) (Agilent E5071B ENA), horn antenna and a computer. The information of heart beat and the breathing can be a shift identification routine was used to collect information from the back scattering electric current. The outcomes of the procedure give the information of heart beat and breathing signs of real human being.

Page 571 of 918 | Total Record : 9174

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