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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 9,199 Documents
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Online social network relationships influenced on a retweeting Iman K. Abbood; Saad Talib Hasson
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp1037-1043

Abstract

Social network users spending a lot of time to post, search, interact and read the news on blogging platforms. In this era, social media is becoming a suitable place for discovering and exchanging new updates. However, Common social media helps the user to share his news online by a one-click. The ease-of-use leads to present novel breaking news to show up first on micro blogs. Twitter is one of the well-known micro blogging platforms with more than 250 million users, in which retweeting is a manageable way to share and sawing news. It is significant to foretell the retweeting and influence in a social relationship. The Correlation Coefficient formula has been used to determine the level of correlation between a user and his retweeters (followers, friends, and strangers) in social networks. Such correlation can be reached by utilizing the collected user information on Twitter with some features which have a main effect on retweet behavior. In this study, the focus is on particular friends, followers, and a retweet to be the promising source of relationships between users of social media. Experimental results based on twitter dataset showed that the Correlation Coefficient formula can be used as a predicting model, and it is a general framework to gain better fulfillment in calculating the correlation between the user, friends, and followers in social networks..  Their influence on the accuracy in predicting a retweet is also accomplished.
Performance simulation of the integration of hybrid stand-alone photovoltaic system at Tuba Island Shahril Irwan Sulaiman; Nur Amira Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp107-115

Abstract

Hybrid photovoltaic-diesel generator power system is important for rural electrification with the diesel generator supplying electricity when battery bank fails to meet the load demand. However, the operation of diesel generator could also be very costly due to high operation and maintenance cost when compared to photovoltaic-battery system. As a result, proper sizing must be conducted to determine the economic indicators of the hybrid photovoltaic-diesel generator system throughout its lifetime. This paper presents the design of such system for an island resort in Langkawi, Kedah, Malaysia. HOMER software was used to simulate the design parameters and economic performance of the system as compared to the existing diesel generator system. Apart from that, different capacities of PV array, battery bank and inverter were investigated in this study to determine the optimum configuration of these components such that the total cost of supplying the load demand at the resort could be minimized. The results showed that the hybrid photovoltaic-diesel generator system is more economically feasible than the existing diesel generator system used at the resort.
Combination of Fault Tree and Neural Networks in Excavator Diagnosis Li Guoping; Zhang Qingwei; Ma Xiao
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 4: April 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA) and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2333
Improved scaling of spectrum decision mechanism in cognitive radio networks Peng Yi; Hou Yong Ji; Long Hua; Li Hai Tao
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 9: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i9.pp6892-6896

Abstract

In cognitive radio networks,We usually use nine scaling law based on multi-attribute utility theory as a common spectrum decision mechanism.the traditional scale method only consider the spectrum own objective properties and then makes judgments is not comprehensive enough. This paper through researched the mechanism of scaling judgment in psychology, a new and improved scale method were proposed, and the new scale method applied to spectrum decisions ofcognitive radio networks, So that the measure of subjective judgment factors are more scientific and makes more explicit determination in spectrum decisions. Finally through calculate consistency index to verify the superiority of the improved scaling.
E-commerce Website Recommender System Based on Dissimilarity and Association Rule LiFeng Zhang; ShuWen Yang; MingWang Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: January 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

By analyzing the current electronic commerce recommendation algorithm analysis, put forward a kind to use dissimilarity clustering and association recommendation algorithm, the algorithm realized web website shopping user data clustering by use of the dissimilarity, and then use the association rules algorithm for clustering results of association recommendation, experiments show that the algorithm compared with traditional clustering association algorithm of iteration times decrease, improve operational efficiency, to prove the method by use of the actual users purchase the recommended, and evidence of the effectiveness of the algorithm in recommendation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.4002
Short-term wind speed prediction based on MLP and NARX network models Yousra Amellas; Outman El bakkali; Abdelouahed Djebli; Adil Echchelh
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp150-157

Abstract

The article aims to predict the wind speed by two artificial neural network’s models. The first model is a multilayer perceptron (MLP) treated by back-propagation algorithm and the second one is a recurrent neuron network type, processed by the NARX model. The two models having the same Network’s structure, which they are composed by 4 Inputs layers (Wind Speed, Pressure Temperature and Humidity), an intermediate layer defined by 20 neurons and an activation function, as well as a single output layer characterized by wind speed and a linear function. NARX shows the best results with a regression coefficient R = 0.984 et RMSE = 0.314.
Scheduling Two-machine Flowshop with Limited Waiting Times to Minimize Makespan Bailin Wang; Tieke Li; Cantao Shi; Haifeng Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

There are numerous instances of flowshop in the production process of process industry. When such characteristics as continuous production resulted from high-temperature environment or deteriorate intermediate products are took into consideration, it should be ensured that the waiting time of any job between two consecutive machines is not greater than a given value, which results in the flowshop scheduling problem with limited waiting time constraints. The problem with two-machine environment to minimize makespan is studied. Based on the discussion of the lower bound of the minimal makespan and some properties of the optimal schedule, a two-stage search algorithm is proposed, in which the initial schedule is generated by a modified LK heuristic in the first stage and the excellent solution can be obtained by constructing inserting neighborhood in the second stage. The numerical results demonstrate the effectiveness of the algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4783 
Study on Software Quality Improvement based on Rayleigh Model and PDCA Model Ning Jingfeng; Hu Ming
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 8: August 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

As the software industry gradually becomes mature, software quality is regarded as the life of a software enterprise. This article discusses how to improve the quality of software, applies Rayleigh model and PDCA model to the software quality management, combines with the defect removal effectiveness index, exerts PDCA model to solve the problem of quality management objectives when using the Rayleigh model in bidirectional quality improvement strategies of software quality management, and puts it into the application to achieve good results. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3086 
Performance of channel selection used for Multi-class EEG signal classification of motor imagery Djelloul Kheira; M. Beladgham
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1305-1312

Abstract

In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the electrode positions varied from subject to subject; moreover , using one subset of the channels enhanced the classification performances compared to literature data. an average accuracy of 86.06% was observed among all 3 subjects.
A computing model for trend analysis in stock data stream classification Abdul Razak; Nirmala C. R
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1602-1609

Abstract

For several decades, many statistical and scientific efforts took place for the better analysis or prediction of stock trading. But still it is open to offer new avenues for the scientists to rethink and discover new inferences by adopting latest technological scenarios. In this regard, this paper is trying to apply classification techniques on stock data stream through feature extraction for the trend analysis. The proposed work is involving k-means for clustering samples into two clusters (the stocks in trend as one cluster and another on as stocks not in trend). The trend analysis is done based on density estimation of the stocks with respect to sectors. A well-known data representation method that is histogram is used to represent the sector which is in trend. This work has been implemented and experimented by considering live NSE (India) data using python and its related tools.

Page 419 of 920 | Total Record : 9199

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