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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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A Multi-objective QoS Mechanism for Web Applications: Improved EFXCP Hangxing Wu; Xiaolong Yang; Min Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: February 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

The research of QoS is concerned with multiple QoS requirements (multi-QR): queue delay, delay jitter and guaranteed throughput etc. As different QoS requirements have different attributes, it is hard to ensure multi-QR in one QoS mechanism. A common method to ensure multi-QR is normalizing multi-QR into one value range by a utility function and then finding the optimal solution of the utility function. However, although multi-QR are considered in such QoS mechanisms, in fact, no concrete QoS requirement can been strictly ensured, which may be embarrassing because web application usually requires one or more concrete QoS requirements to be satisfied strictly. Therefore, it is significant to design a real multi-objective QoS mechanism to satisfy different QoS requirements of various web applications. Our previous works proposed an efficient and fair explicit congestion control protocol (EFXCP) which can achieve excellent performance in terms of high link utilization, low queue delay, low delay jitter, etc. Because different web applications have different throughput requirements, to further satisfy throughput requirements of web applications, we extend EFXCP in this paper to implement an improved EFXCP (IEFXCP). Firstly, ToS field in IP header is utilized to classify different web applications, and then the relative fair bandwidth allocation is proposed between different types of web applications to preferentially ensure throughput requirements of high prior web applications.  Therefore, IEFXCP can simultaneously satisfy multi-QR: queue delay, delay jitter and guaranteed throughput. The performance of IEFXCP is validated by extensive NS2 simulations over a wide range of network scenarios, the results show that IEFXCP is a real multi-objective QoS mechanism. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4009 
Power of Ambient Tempurature on the Performance of the Semiconductor Laser J. S. Ashwin; N. Manoharan
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 3: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i3.pp606-608

Abstract

In this paper, the impact of surrounding temperature on the execution of the semiconductor laser is systematically examined in principle. We constructed the recreation display and the simulation results about showed that ambient temperature changes would influence the laser chip temperature, bringing about the laser key parameters in reproduction comes about.
Extraction of cause-effect-concept pair series from web documents Chaveevan Pechsiri; Titirut Mekbunditkul
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 2: May 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i2.pp970-978

Abstract

This research aims to extract a cause-effect-concept pair series of consequent event occurrences in health information of hospital web-boards. The extracted cause-effect-concept pair series representing a disease causation pathway benefits for the automatic diagnosis and solving system. Where each causative/effect event concept is expressed by an elementary discourse unit (EDU which is a simple sentence). The research has three problems; how to determine causative/effect concept EDUs from the documents containing some EDU occurrences with both causative concepts and effect concepts, how to determine the cause-effect relation between two adjacent EDUs having the discourse cue ambiguity, and how to extract cause-effect-concept pair series mingled with either a stimulation relation EDU or other non-cause-effect relation EDUs from the documents. Therefore, we apply annotated NWordCo pairs with causative-effect concepts to represent EDU pairs with causative-effect concept where the NWordCo size solved by Naïve Bayes. We also apply Naïve Bayes to solve NWordCo-concept pairs having the cause-effect relation from the adjacent EDU pairs. We then propose using cue words and the collected NWordCo-concept pairs with the cause-effect relation to extract the cause-effect-concept pair series. The research results provide the high precision of the cause-effect-concept pair series determination from the documents. 
A New Systemic Safety Detecting Software Xilong Qu; Yingjun Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 10: October 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Because it is hard to find and to clear cockhorse and virus developed by root kit technology, antivirus soft at present is hard to clear virus in the system, which make the system in dangers status of hazard. So, designing a speedy clear Trojan and virus makes by root kit is very important. The article.is based on SDK, adopting the technology.of kernel to design the Clairvoyant systemic safety detecting software. It major function is monitors.the service of the system and the operation. Monitor the register changer. Search the file, process, system module hided by the virus. It can also end protected processes and delete protected files forcibly. Through the port.mapping of processes, it can find.port messages opened.by system, processes opening ports and.cockhorse effectively. http://dx.doi.org/10.11591/telkomnika.v12i10.5345 
Evaluation of basic convolutional neural network and bag of features for leaf recognition Nurul Fatihah Sahidan; Ahmad Khairi Juha; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp327-332

Abstract

This paper presents the evaluation of basic Convolutional Neural Network (CNN) and Bag of Features (BoF) for Leaf Recognition. In this study, the performance of basic CNN and BoF for leaf recognition using a publicly available dataset called Folio dataset has been investigated. CNN has proven its powerful feature representation power in computer vision. The same goes with BoF where it has set new performance standards on popular image classification benchmarks and has achieved scalability breakthrough in image retrieval. The feature that is being utilized in the BoF is Speeded-Up Robust Feature (SURF) texture feature. The experimental results indicate that BoF achieves better accuracy compared to basic CNN.
Mapping of Flooded Areas in the Kudus District Rina Fiati; Anastasya Latubessy
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 3: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i3.pp670-677

Abstract

Flood is still an annual problem in the Kudus District. Based on the survey and interview with Regional Disaster Management Agency (BPBD – Badan Penanggulangan Bencana Daerah) data showed that in the Kudus District there are still many flood-prone areas. They also said that, there are six parameter that can be used to identify potential flood area such as: extensive inundation (km2, ha), depth or height of flood waters (meters), the flow velocity (m/s,km/h), the material washed away by flood flow (rocks, boulders, trees, and other solid objects), concentrations of water or silt thickness (meters, centimeters), and duration of inundation (hours, days, months). Therefor this research use six parameters are then analyzed and used as a benchmark model to identify flood-prone areas by using the production rule method, and as the material in constructing and designing flood-prone area identification systems based on expert system. Thus this research resulted a system to assist the identification of flood prone areas in the Kudus District by using expert system and geographic information system (GIS).
CAPSOCA: hybrid technique for nosologic segmentation of primary brain tumors Shafaf Ibrahim; Noor Elaiza Abd Khalid; Mazani Manaf
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.pp267-274

Abstract

Detection of primary brain tumors is inspired by the necessity of high accuracy as it deals with human life. Various imaging modalities techniques have incarnated as a tool in diagnosis and treatment domain. Yet, experienced and competent medical practitioners for the proper interpretation are still required. Thus, the involvement of information technology is highly demanded in introducing reliable and accurate computer systems. This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. Nosologic refers to the classification of diseases that can facilitate the diagnosis of neurological diseases. The purpose of segmentation is to highlight the tumor areas, whereas classification is used to identify the type of the primary brain tumors. For this purpose, an algorithm which hybridized the Grey Level Co- occurrence Matrices (GLCM), Intensity Based Analysis (IBA), Adaptive Network-based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) Clustering Algorithm (CAPSOCA) is proposed. The combination of several computer vision techniques is aim to deliver reproducible nosologic segmentation of primary brain tumors which are gliomas and meningiomas. The performance of the CAPSOCA is quantified by two measurements which are segmentation and classification accuracy. The segmentation accuracy is evaluated using comparison with ground truth approach. On the other hand, the classification accuracy is quantified using a truth table by comparing the classification outcomes with histopathology diagnosis. Upon the testing conducted, the CAPSOCA was proven to be an effective algorithm for nosologic segmentation of primary brain tumors. It appeared to return 88.09% of overall mean accuracy for gliomas segmentation, 86.92% of overall mean accuracy for meningiomas segmentation. In another note, 83.72% and 85.19% of classification accuracy for gliomas and meningiomas were observed.
Electro-thermal Modeling of Lithium ion Batteries Gaoussou Hadia Fofana; Youtong Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 5: May 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, the electro-thermal model of Lithium-ion battery for electric vehicles and its related application were studied. The spatial variations of electrode parameter and the reaction heat generated inside battery must be considered when developing an electro-thermal model of Lithium-ion battery for electric vehicles, to ensure the applicability of the developed model under different operating conditions. The results showed that: with increasing state of charge, the spatial variations of net reaction current density, lithium ion concentration on the surface of active material particles, activation overpotential, equilibrium electrode potential and electrical potential of solid phase are reduced, but the spatial variation of electrical potential of electrolyte phase is enlarged. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5016
Discrete Chicken Swarm Optimization for the Quadratic Assignment Problem Soukaina Cherif Bourki Semlali; Mohammed Essaid Riffi; Fayçal Chebihi
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i3.pp925-935

Abstract

The main objective of our research is to improve an adaptation of the chicken swarm optimization algorithm (CSO) to solve the quadratic assignment problem, which is a well-known combinatorial optimization problem. The new approach is based on the CSO without using a local search, the CSO-QAP is a stochastic method inspired from the behavior of chickens in swarm while searching for food. The experiments are performed on a set of 56 benchmark QAPLIB instances. To prove the robustness of our algorithm a comparative analysis is done with the known metaheuristic of Genetic algorithm based on SCX. The average percentage of error to get the best Known solution in our proposed work with the results obtained by applying a simple genetic algorithm using sequential constructive crossover for the quadratic assignment problem. The results show the effectiveness of the proposed CSO-QAP to solve the Quadratic assignment problem in term of time and quality of solutions. The proposed adaptation can be further applied by using a local search strategy to solve the same problem or another combinatorial problem.
Hybrid order characteristics in car-following behavior Chunling Tu; Shengzhi Du
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp158-166

Abstract

This paper addresses the discovery of an interesting property in car-following processes, which was not reported in the existing literatures. A hybrid order behavior is supported by both experimental data and theoretical simulations. To demonstrate this behavior, the first order and the second order car-following behaviors are defined. Then, by comparing the first and the second order car-following behaviors in the existing analystic models and the real traffic context, this paper finds that a significant amount of the second order car-following processes in real traffic context do not match the existing models and structural mismatches are observed. The popularity and significance of such cases suggest the existence of unmodelled dynamics in the existing methods, that is, the car following behavior should be determined by more factors than the immediate proceeding vehicle. Therefore, the existing car-following models must be improved to accommodate these factors. This forms one of the main values of this paper. This paper then defines the hybrid order car-following behavior and prompts to associate this behavior with the concerned unmodelled dynamics (mismatches between the actual traffic data and the simulation from models). The neural network is employed to model such dynamics. The idea of the proposed hybrid order behavior matches the fact that the car-following behavior is determined by multiple vehicles driving in front of the subject car instead of only the immediate proceeding one. This is valuable because it provides guidance on the improvement of existing car-following models. The neural network model validates that the consideration of multiple vehicles improves the accuracy of car-following modelling.

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