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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PENINGKATAN FEATURE SELECTION DENGAN WINDOWED MOMENTUM UNTUK PREDIKSI KANKER PAYUDARA
Keywords:
Kanker payudara, Neural Network, Data Mining
Abstract
Kanker payudara meningkat di setiap negara di dunia , terutama di negara-negara berkembang seperti Indonesia . Neural Network mampu memecahkan masalah dengan akurasi data dan tidak linear . Neural Network optimasi diuji minggu untuk menghasilkan yang terbaik nilai akurasi , menerapkan jaringan saraf dengan metode seleksi fitur seperti Wrapper dengan Penghapusan Mundur untuk meningkatkan akurasi yang dihasilkan oleh Neural Network. Percobaan yang dilakukan untuk mendapatkan arsitektur yang optimal dan meningkatkan nilai akurasi . Hasil dari penelitian ini adalah matriks kebingungan untuk membuktikan keakuratan Neural Network sebelum dioptimalkan oleh Backward Elimination adalah 96,42 % dan 96,71 % setelah menjadi dioptimalkan. Hal ini membuktikan estimasi uji seleksi fitur menggunakan metode berbasis jaringan saraf Backward Elimination lebih akurat dibandingkan dengan metode jaringan saraf tiruan. Windowed momentum dapat meningkatkan waktu pengklasifikasian feature selection sehingga didapat momentum yang lebih maksimal.
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Bevan, Nigel. (1997). Quality and Usability: A New Framework. National Physical Laboratory. UK.
Ciampi, Antonio. Zhang, Fulin. (2002). A New Approach to Training Backpropagation Artificial Neural Network: Empirical Evaluation on Tens Dataset on Clinical Studies. McGill University. Canada.
Gorunescu, Florin. (2011). Data Mining: Concepts, Models and Techniques. Verlag Berlin Heidelberg, Springer. Jerman.
Guillet, Fabrice. Hamilton, Howard J. (2007). Quality Measures in Data Mining. Verlag Berlin Heidelberg, Springer. Jerman.
Han,J & Kamber, Micheline. (2007). Data Mining Concepts, Models and Techniques. Second Edition, Morgan Kaufmann Publisher. Elsevier.
Heaton, Jeff. (2010). Programming Neural Networks With Encog 2 In Java. Heaton Research.Inc, USA.
Hong, X., Harris, C., Brown, M., & Chen, S. (2002). Backward Elimination Methods for Associative Memory Network Pruning. Computers and Technology, (Reed 1993).
Istook, Martinez. (2002). Improved Backpropagation Learning in Neural Networks with Windowed Momentum. International Journal of Neural System, Vol 12, no 3&4, pp 303-318.
Kadhim, Jehan & Abdulrazzaq, Mohammad (2015).
Forecasting USD/IQD Future Values According to Minimum RMSE Rate. Thi_Qar University. pg.271–285
Kohavi, R., & John, H. (1997). Artificial Intelligence Wrappers for feature subset selection. elsevier, 97(97), 273–324.
Kusumadewi, Sri & Hartati, Sri. (2010). Neuro-Fuzzy Integrasi Sistem Fuzzy & Jaringan Syaraf. Second Edition. Yogyakarta: Graha Ilmu.
Lakshmi, R, Raju Athira, Joy Teena Mary, S. Vijayalakshmi. (2012). Breast Cancer Factor Preventable and Non-Preventable. Departement of Pharmacy Practice. India.
Larose, D. (2005). Discovering Knowledge in Data. New Jersey, John Willey & Sons.Inc.
Liao, Warren. T. & Triantaphyllou. Evangelos. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Series: Computer and Operation Research. 6. 190.
Lim TS, Loh WY, Shih YS. (1999). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Kluwer Academic Publishers: Boston.
Liu, Huan, Yu, Lei.(2005). Toward Integrating Feature Selection Algorithms for Classification and Clustering. Department of Computer Science and Engineering. Arizona State University.
Liu, Yuaning, Wang G., Chen, M., Dong, M., Zhu, X., Wang, S. (2011). An Improved Particle Swarm Optimization for Feature Selection. College of Computer Science and Technology. China.
Maimon, Oded & Rokach, Lior. (2010). Data Mining and Knowledge Discovery Handbook, Springer, New York.
Myatt, Glenn J. (2007). Making sense of data : A Practical Guide to Exploratory data analysis and Data Mining. John Wiley & Sons Inc, New Jersey.
Shukla, Anupam. Tiwari, Ritu. & Kala, Rahul. (2010). Real Life Application of Soft Computing. New York: Taylor and Francis Groups, LLC.
Siang, Jong Jek (2009). Jaringan Syaraf Tiruan dan Pemrogramannya menggunakan MATLAB. Penerbit Andi. Yogjakarta.
Vercellis,C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. Wiley.
Witten,I. Frank, E., & Hall. (2011). Data Mining: Practical Machine Learning and tools. Morgan Kaufmann Publisher, Burlington.
Published
2016-09-15
How to Cite
Priyanti, E. (2016). PENINGKATAN FEATURE SELECTION DENGAN WINDOWED MOMENTUM UNTUK PREDIKSI KANKER PAYUDARA. Jurnal Pilar Nusa Mandiri, 12(2), 130-139. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/267
Section
Articles
Copyright (c) 2016 Evy Priyanti
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