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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 16, No 2: November 2019" : 65 Documents clear
Traffic-based floor preference for the scheduling of elevators in elevator group control system Malan Dipak Sale; V. Chandra Prakash
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp835-842

Abstract

Modern high-rise buildings require complex yet efficient Elevator Group Control Systems (EGCSs). In vertical transportation through an elevator, a passenger must make a hall call by pressing a landing call button installed at each floor and located near the cars of the elevator group. Conventionally, the EGCS allocates one of the cars for each hall call. Waiting time for the arrival of car and journey time inside a car are two parameters, which provide a suitable measure for quality and efficiency of EGCSs. The proposed system deals with this car-call allocation problem. The proposed work analyzes the generated traffic patterns to dispatch a certain number of cars to certain floors in order to reduce overall wait time of passengers. The proposed algorithm is simulated for high-rise building with 20 floors and provides a better result with the reduced wait time for more number of passengers.
A two-step feature selection method for quranic text classification A. Adeleke; N. A. Samsudin; Z. A. Othman; S. K. Ahmad Khalid
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp730-736

Abstract

Feature selection is an integral phase in text classification problems. It is primarily applied in preprocessing text data prior to labeling. However, there exist some limitations with the FS techniques. The filter-based FS techniques have the drawback of lower accuracy performance while the wrapper-based techniques are highly computationally expensive to process. In this paper, a two-step FS method is presented. In the first step, chisquare (CH) filter-based technique is used to reduce the dimensionality of the feature set and then wrapper correlation-based (CFS) technique is employed in the second step to further select most relevant features from the reduced feature set. Specifically, the ultimate aim is to reduce the computational runtime while achieving high classification accuracy. Subsequently, the proposed method was applied in labeling instances of the input data (Quranic verses) using standard classifiers: naïve bayes (NB), support vector machine (SVM), decision trees (J48). The results report the proposed method achieved accuracy result of 93.6% at 4.17secs.
Analytics of stock market prices based on machine learning algorithms Puteri Hasya Damia Abd Samad; Sofianita Mutalib; Shuzlina Abdul-Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp1050-1058

Abstract

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.
Outage probability analysis of dual energy harvesting relay network over rayleigh fading channel using SC and MRC technique Tan N. Nguyen; Minh Tran; Van-Duc Phan; Tran Thanh Trang
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp803-811

Abstract

In this paper, the system model of dual-energy harvesting relay network over Rayleigh fading channel and the comparison between Selecting Combining (SC) and Maximal Ratio Combining (MRC) technique cases are proposed and investigated. The closed-form expression of the outage probability for the SC case and the integral-form expression of the outage probability for MRC case is derived. Moreover, the influence of the main parameters on the system performance is demonstrated entirely by the Monte Carlo simulation. From the results, we can see that all simulation and analytical results match well with each other.
Projective synchronization for a cass of 6-D hyperchaotic lorenz system Ahmed S. Al-Obeidi; Saad Fawzi AL-Azzawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp692-700

Abstract

This paper is concerned with the projective synchronization problem for a class of 6-D nonlinear dynamical system which is called hyperchaotic Lorenz system when the parameters of this system are unknown. Based on scaling factor  which belong to above strategy, four controller are proposed to achieve projective synchronization between two identical systems via using Lyapunov's direct method and nonlinear control strategy.  These scaling factor taken the values, and 2 for each control respectively. A numerical simulations are used to demonstrate the efficiency of the proposed controller.

Page 7 of 7 | Total Record : 65

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