Arrangement of Players Position in Soccer Using the Technique of Naive Bayes

Authors

  • Gusti Made Trisetya Putra Kalbis Institute of Technology and Business
  • Muhammad Rusli Kalbis Institute of Technology and Business

DOI:

https://doi.org/10.21512/comtech.v6i4.2204

Keywords:

analysis and design, data mining, decision support system, naive bayes, soccer

Abstract

In the modern soccer era, soccer is already considered as an entertainment, even modern soccer already become as an industry or a business that considered can bring a great profit to the club owner. One of the most important factor in building a team is young age soccer player development. Right young age soccer player development method, can be very helpful in establish a good team. A professional team must have a
coach, for the first team or junior team. The duties of a coach is determine a right position for soccer player in the game, this duties sometimes make a coach is hard to making a right decision. This research will discuss
about how to design a decision support system for determine soccer player using naive bayes technique. Data mining used naive bayes technique for find a prediction for soccer player based on the player skill test result. From this research result, it can be seen that by using decision support system using data mining with naive bayes technique can be help coach performance in determine position for soccer player especially for young age soccer player development so that can help coach in the making right decision effectively and efficiently.

Dimensions

Plum Analytics

References

B. Santosa.(2007). Data Mining Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu. Davies & P. Beynon. Database Systems. 3rd Edition. New York : Palgrave Macmillan, 2004.

E. Turban. et al. (2005). Introduction to Information Technology. 3rd Edition. USA: John Wiley & Sons, Inc.

G. A. Gorry & M.S.Morton. (1971). A Framework for Management Information Systems. USA: Sloan Management Review.

G. Banerji and K. Saxena. (2012). A Comparative Analysis Of Bayesian Methods For Real Estate Domain. International Journal Physical and Social Science, 2(9): 554 – 565.

H. Ian et. al. (2005). Data Mining: Practical machine learning tools and techniques. 2nd Edition. San Francisco: Morgan Kaufmann.

I. Pramudiono. (2006). Pengantar Data Mining: Menambang Permata Pengetahuan di Gunung Data. Diakses 4 September 2014 dari http://www.ilmukomputer.org/wpcontent/uploads/2006/08/iko-datamining.zip.

J. E. Kendall & K. E. Kendall. (2005). System Analysis and Design. 6th Edition. New Jersey: Pearson Education.

J. Han & Kamber, M. (2006). Data Mining: Concept and Techniques. 2nd Edition. San Francisco: Morgan Kaufmann Publishers.

Kusrini. (2007). Konsep dan Aplikasi Sistem Pendukung Keputusan. Yogyakarta: CV Andi Offset.

K. M. Leung. (2007). Naive Bayesian Classifier. Diakses 4 September 2012

http://cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf

K. Suryadi& A. Ramdhani. (1998). Sistem Pendukung Keputusan. Bandung: PT. Remaja Rosdakarya. Munawar. (2005). Pomodelan Visual dengan UML. Yogyakarta : Graha Ilmu.

M. Fowler. (2005). UML Distilled. 3rd Edition. Yogyakarta: Andi.

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Published

2015-12-01

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Articles
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