Analisis perbandingan Reduction Technique dengan metode Dimentional Reduction dan Cross Validation pada dataset Breast Cancer
Abstract
Machine learning (ML) merupakan bidang ilmu yang memungkinkan komputer dalam mengembangkan sebuah sistem yang dapat belajar dari data. Dalam ML sendiri banyak teknik sangat berperan penting dalam pengembangan machine ML salah satunya adalah teknik reduksi yang dimana membuat sistem lebih baik dari data yang telah di reduksi. Penelitian ini bertujuan membandingkan performa teknik reduksi dengan metode dimentional reduction dan cross validation pada dataset breast cancer. Dimentional reduction merupakan teknik yang menyederhanakan feature atau mengurangi dimensi pada dataset sedangkan cross validation merupakan metode yang digunakan untuk memaksimalkan hasil dari prediksi pada suatu model. Setalah melakukan tahapan-tahapn dalam pengujian dengan dimentional reduction dan cross validation menggunakan algoritma K-Nearest Neighbors dengan dataset breast cancer berjumlah 500. Hasil yang diperolah untuk dimentional reduction akurasi rata-rata pada model 95.2%, sedangkan pada cross validation 96.6%.
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References
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