TELKOMNIKA (Telecommunication Computing Electronics and Control)
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
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Hadoop Performance Analysis on Raspberry Pi for DNA Sequence Alignment
Jaya Sena Turana;
Heru Sukoco;
Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.1886
The rapid development of electronic data has brought two major challenges, namely, how to store big data and how to process it. Two main problems in processing big data are the high cost and the computational power. Hadoop, one of the open source frameworks for processing big data, uses distributed computational model designed to be able to run on commodity hardware. The aim of this research is to analyze Hadoop cluster on Raspberry Pi as a commodity hardware for DNA sequence alignment. Six B Model Raspberry Pi and a Biodoop library were used in this research for DNA sequence alignment. The length of the DNA used in this research is between 5,639 bp and 13,271 bp. The results showed that the Hadoop cluster was running on the Raspberry Pi with average usage of processor 73.08%, 334.69 MB of memory and 19.89 minutes of job time completion. The distribution of Hadoop data file blocks was found to reduce processor usage as much as 24.14% and memory usage as much as 8.49%. However this increased job processing time as much as 31.53%.
Arduino Based Paperless Queue Management System
Aiman Zakwan Jidin;
Norfadzlia Mohd Yusof;
Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3114
Queue management system is designed in organizing queues at service sectors like banks and post offices, which expected to have a large number of customers daily. Conventional ways of managing queues like issuing paper tickets printed with queue number, leads to several problems such as paper tickets littering and also long queueing or waiting time. Therefore, this paper presents the development of a system to manage queues more efficiently and eco-friendly. The proposed system consists of a Graphical User Interface (GUI), which is used to obtain customers’ mobile phone numbers and the processing unit, which generates the queue number and initiate the ticket to be sent to customers’ mobile phones via SMS, thus replacing the utilization of papers. Moreover, this system additional features allow customers to remotely obtain their queue number just by sending request to the system through SMS, and also reminding the upcoming customers that their turns are nearly arriving, a feature which is very useful especially for those who are waiting outside the premise. Simulations and experimental tests were conducted to ensure the reliability and the efficiency of the proposed system. The proposed system is supporting the development of sustainable green technology, and the expected increase of system efficiency may contribute in improving customers’ satisfaction.
Application of Nonlinear Dynamical Methods for Arc Welding Quality Monitoring
Shuguang Wu;
Yiqing Zhou
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3589
Owing to its diverse, the stability of arc signals in high-powered submerged arc welding is not very salient, and weld defects are difficult to detect automatically. Aimed at this problem, this paper proposes a noise robustness algorithm for calibrating the singularity points and denoting the kinetics and stability of arc. Firstly, reconstruct a vector, which is the calculation of the approximate entropy in phase space, denotes the distortion of arc. Then, a algorithm for calculation is given based on reconstruction of chaotic time series in phase space. Finally, we apply the calculation of approximate entropy algorithm in phase space to flaw detection for arc signals, which is efficient proved by experimental results.
An Improved Artificial Bee Colony Algorithm for Staged Search
Shoulin Yin;
Jie Liu;
Lin Teng
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3609
Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions.
Design and Fabrication of Compact MEMS Electromagnetic Micro-Actuator with Planar Micro-Coil Based on PCB
Roer Eka Pawinanto;
Jumril Yunas;
Burhanuddin Majlis;
Azrul Hamzah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3998
This paper reports a compact design of electromagnetically driven MEMS micro-actuator utilizing planar electromagnetic coil on PCB (Printed Circuit Board). The micro-actuator device consists of an NdFeB permanent magnet, thin silicon membrane and planar micro-coil which fabricated using simple standard MEMS techniques with additional bonding step. Two planar coils designs including planar parallel and spiral coil structure with various coil geometry are chosen for the study. Analysis of the device involves the investigation of electromagnetic and mechanical properties using finite element analysis (FEA), the measurement of the membrane deflection and functionality test. The measurement results show that the thin silicon membrane is able to deform as much as 12.87 µm using planar spiral micro-coil. Reasonable match between simulation and measurement of about 82.5% has been revealed. The dynamic response test on actuator driven by parallel planar coil shows that silicon membrane effectively deformed in 40 s for an input electrical power of only 150 mW. It is also concluded that planar parallel coil is considered for the simple structure and easy fabrication of the actuator system. This study will provide important parameters for the development of compact and simple electromagnetic micro-actuator system for fluidic injection system in lab-on-chip.
Multi-Criteria in Discriminant Analysis to Find the Dominant Features
Arif Muntasa;
Indah Agustien Siradjuddin;
Rima Tri Wahyuningrum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3472
A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra. Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP).
Action Recognition of Human’s Lower Limbs Based on a Human Joint
Feng Liang;
Zhili Zhang;
Xiangyang Li;
Yong Long;
Zhao Tong
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3556
In order to recognize the actions of human’s lower limbs, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and human action characteristics were achieved based on filtering and wavelet transform. Secondly, an improved self-organizing competitive neural network was proposed, which could classify the action characteristics automatically according to the classification number. The classification results of motion capture data proved the validity of the neural network. Finally,an action recognition method based on hidden Markov model (HMM) was introduced to realize the recognition of classification results of human action characteristicswith the change direction of y coordinates. The proposed action recognition method needs less action information and has a fast calculation speed. Experiments proved the method hada high recognition rate and a good application prospect.
Particle Swarm Optimization Performance: Comparison of Dynamic Economic Dispatch with Dantzig-Wolfe Decomposition
Mohd Ruddin Ab Ghani;
Saif Tahseen Hussein;
Zanariah Jano;
Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.4054
Economic Dispatch (ED) problem, in practice, is a nonlinear, non-convex type,which has developed gradually into a serious task management goal in the planning phase of the power system. The prime purpose of Dynamic Economic Dispatch (DED) is to minimize generators’ total cost of the power system. DED is to engage the committed generating units at a minimum cost to meet the load demand while fulfilling various constraints. Utilizing heuristic, population-based, and advanced optimization technique, Particle Swarm Optimization (PSO), represents a challenging problem with large dimension in providing a superior solution for DED optimization problem. The feasibility of the PSO method has been demonstrated technically, and economically for two different systems, and it is compared with the Dantzig-Wolfe technique regarding the solution quality and simplicity of implementation. While Dantzig-Wolfe method has its intrinsic drawbacks and positive features, PSO algorithm is the finest and the most appropriate solution. Conventional techniques have been unsuccessful to present compatible solutions to such problems due to their susceptibility to first estimates and possible entrapment into local optima which may complicate computations.
Research on Optimization Strategy to Data Clustered Storage of Consistent Hashing Algorithm
Ningjia Qiu;
Xiaojuan Hu;
Peng Wang;
Huamin Yang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3550
This paper has been withdrawed due to has major technical problems.---------------------------------------------------------------------This paper presents a consistent hashing data optimize multiple copy distributed clustered storage placement strategy, using technology to create a virtual node and aliquots storage area to ensure the balanced distribution of data storage. With the speed of data processing accelerated, it is possible for clusters actual required to complete the expansion, and complete the development of adaptive optimization. Experiments show that the execution speed has effectively improved to ensure the load balance. With possible extensions and reducing problems treatment in actual situation, oscillation has little impact on the load balancing, and the execution time is consistent with the proportion of the load trends.
Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network
Sutikno Sutikno;
Indra Waspada;
Nurdin Bahtiar;
Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v14i3.3486
One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.