PENINGKATAN FEATURE SELECTION DENGAN WINDOWED MOMENTUM UNTUK PREDIKSI KANKER PAYUDARA

  • Evy Priyanti (1*) Komputerisasi Akuntansi AMIK BSI Jakarta

  • (*) Corresponding Author
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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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
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