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INDONESIA
Journal of Telematics and Informatics
Published by IAES Indonesia Section and Partners
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Journal of Telematics and Informatics (e-ISSN: 2303-3703, p-ISSN: 2303-3711) is an interdisciplinary journal of original research and writing in the wide areas of telematics and informatics. The journal encompasses a variety of topics, including but not limited to: The technology of sending, receiving and storing information via telecommunication devices in conjunction with affecting control on remote objects; The integrated use of telecommunications and informatics; Global positioning system technology integrated with computers and mobile communications technology; The use of telematic systems within road vehicles, in which case the term vehicle telematics may be used; The structure, algorithms, behavior, and interactions of natural and artificial systems that store, process, access and communicate information; Develops its own conceptual and theoretical foundations and utilizes foundations developed in other fields; and The social, economic, political and cultural impacts and challenges of information technologies (advertising and the internet, alternative community networks, e-commerce, e-finance, e–governance, globalization and security, green computing, ICT for sustainable development, ICT in healthcare and education, management and policymaking, mobile and wireless communications, peer-to-peer learning, regulation of digital technologies, social networking, special user groups, the 2.0 paradigm, the WWW, etc). The journal is a collaborative venture between Universitas Islam Sultan Agung (UNISSULA), Universitas Ahmad Dahlan (UAD) and Institute of Advanced Engineering and Science (IAES) Indonesia Section.
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "Vol 4, No 2: September 2016" : 3 Documents clear
Data Mining Sales Optimizations Using Sequential Minimal Optimization Algorithm Dedy Kurniadi; Sam Farisa Chaerul Haviana
Journal of Telematics and Informatics Vol 4, No 2: September 2016
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.343 KB) | DOI: 10.12928/jti.v4i1.

Abstract

Tightness of business nowadays requires businessman to be able to develop their business to compete with the other companies, this study was conducted to obtain data accurate on the type of clothing combinations that are favored by the consumers to optimize sales at convection companies, using data mining methods and technique of classification this data is classify into four classes namely, well-liked, liked, enough and dislike. To solve classification problems, this study used Sequential Minimal Optimization (SMO), SMO Algorithm can solve quadratic programming problems without requiring a large matrix and to solving the optimization SMO selected from the smallest optimization in every steps. Optimum accuracy obtained in this study were obtained from Correctly Classified Instance of 80.9% from 3072 record set of well-liked classes that is the class with type of combinations clothes polo and embroidery, then the level of measurement of consistency coefficient values using kappa statistic obtained for 0.73% where the data in the class showed a consistent value, from these data type are most well-liked combinations can optimize sales by 70.3%
Synthesis of Unequally Spaced Linear Micro Strip Rectangular Patch Antenna Array Using Improved Local Search Particle Swarm Optimization Karuna Kumari; P. V. Sridevi
Journal of Telematics and Informatics Vol 4, No 2: September 2016
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1388.315 KB) | DOI: 10.12928/jti.v4i2.

Abstract

Antenna array systems with low side lobe levels are essential for today wireless communication systems. This paper presents the synthesis of unequally spaced linear rectangular micro strip antenna array with minimum side lobe levels using the novel evolutionary algorithm known as improved local search particle swarm optimization (ILSPSO). ILSPSO is a modified version of particle swarm optimization (PSO), in which Gaussian distribution is used to enhance the local search of the PSO. In this paper, ILPSO is applied to optimize the positions of the micro strip antenna elements to suppress the peak side lobe level (PSLL) along with PSO and differential evolution (DE) algorithms. The steps involved in problem formulation along with design examples illustrating the performance of the ILPSO in minimizing the side lobe levels are demonstrated. A 20 and 32 element linear micro strip rectangular patch antenna (MSRPA) element are considered to show the effectiveness of the proposed method. The optimized micro strip antenna array is simulated using high frequency structure simulator (HFSS). The synthesis results demonstrate that the ILSPSO outperforms PSO and DE in terms of producing lower PSLL and convergence rate. The flexibility and ease of implementation of the ILSPSO algorithm is obvious from this paper, showing the algorithms usefulness in other array synthesis problems.
Machine Learning Approaches on External Plagiarism Detection Imam Much Ibnu Subroto; Ali Selamat; Badieah Assegaf
Journal of Telematics and Informatics Vol 4, No 2: September 2016
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (579.768 KB) | DOI: 10.12928/jti.v4i2.

Abstract

External plagiarism detection is a technique that refers to the comparison between suspicious document and different sources. External plagiarism models are generally preceded by candidate document retrieval and further analysis and then performed to determine the plagiarism occurring. Currently most of the external plagiarism detection is using similarity measurement approaches that are expressed by a pair of sentences or phrase considered similar. Similarity techniques approach is more easily understood using a formula which compares term or token between the two documents. In contrast to the approach of machine learning techniques which refer to the pattern matching and cannot directly comparing token or term between two documents. This paper proposes some machine learning techniques such as k-nearest neighbors (KNN), support vector machine (SVM) and artificial neural network (ANN) for external plagiarism detection and comparing the result with Cosine similarity measurement approach. This paper presented density based that normalized by frequency as the pattern. The result showed that all machine learning approach used in this experiment has better performance in term of accuracy, precision and recall.

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