PENERAPAN METODE NEURAL NETWORK BERBASIS PARTICLE SWAM OPTIMIZIED UNTUK PREDIKSI KESUBURAN PADA PRIA

HILDA AMALIA

Abstract


The fertility rate is to be considered, especially for adult men is because infertility is a global problem that occurs in couples. Infertility can result in couples unable to conceive. Infertility in one partner may terminate especially male lineage. Its high level of infertility in today's society can be caused by several things between environmental factors and lifestyle of today's society. Previous research on fertility has been done is by using artificial neural network and produce 82% accuracy. In this paper will be improving the accuracy of neural network method for predicting male fertility through semen using particle swam optimazed (PSO). The survey results revealed that the use of the optimization method can improve the accuracy of the method used in this study was obtained accuracy of new methods for neural network improved its performance using PSO to 92%.


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References


Durairaj & Sathyavathi. (2013). Applying Rough Set Theory for Medical Informatics Data Analysis. International Journal of Scientific Research in Computer Science and Engineering, Vol-1, Issue-5 ISSN: 2320-7639

Gilera et. (2012). Predicting seminal quality with artificial intelligence methods, Expert Systems with Applications 39 (2012) 12564–12573

Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San

Fransisco: Mofgan Kaufan Publisher Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San Fransisco: Mofgan Kaufan Publisher

Inhorn. (2002). Global infertility and the globalization of new reproductive technologies: illustrations from Egypt, Social Science & Medicine 56 (2003) 1837–1851, Elsevier Science Ltd. All rights reserved.

Kahki etc. (2013). A model based on Bayesian Network for prediction of IVF Success Rate, 7thSASTech 2013, Iran, Bandar-Abbas. 7-8 March

Kolettis, P. N. 2003. Evaluation of the subfertile man. American Family Physician, 67(10), 2165–2172

Larose, D. T. (2005). Discovering Knowledge in Databases. New Jersey: John Willey & Sons Inc.Myatt, Glenn J. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.

Mileski etc. (2013). Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment, STUDIES IN LOGIC, GRAMMAR AND RHETORIC 35 (48), ISBN 978–83–7431–392–6 ISSN 0860-150X DOI: 10.2478/slgr-2013-0033

Maimon. (2005). Data Mining and Discovery Handbook Second Edition, ISBN 978-0-387-09822-7 e-ISBN 978-0-387-09823-4, Springer, New York

Sivanandam. (2006). Introduction to Data Mining and its Applications, Springer, New York.

Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Application of Soft Computing. Taylor and Francis Groups, LLC.

Vecellis. (2009). Business Intelligence: Data Mining and Optimization for Decision Making, Ltd. ISBN: 978-0-470-51138-1, United Kingdom, John Wiley & Sons




DOI: https://doi.org/10.31294/p.v17i1.738

ISSN2579-3500

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