Perbandingan Performa antara Imputasi Metode Konvensional dan Imputasi dengan Algoritma Mutual Nearest Neighbor

Azwar Rizal Alfarisi, Handayani Tjandrasa, Isye Arieshanti
Submission Date: 2013-02-11 21:02:49
Accepted Date: 2013-03-01 00:00:00

Abstract


Missing value adalah sebuah permasalahan yang sering terjadi pada dataset riil. Kekurangan ini biasanya mempengaruhi akurasi saat dilakukan klasifikasi dengan menggunakan dataset tersebut. Salah satu cara menyelesaikan masalah missing value tersebut adalah mengisi nilai baru atau dikenal dengan metode imputasi. Algoritma mutual nearest neighbor (MNN) adalah sebuah algoritma pengenalan pola yang menggunakan tetangga mutual terdekat suatu instance. Dalam studi ini, algoritma MNN digunakan sebagai metode imputasi. Performanya akan dibandingkan dengan metode imputasi konvensional yaitu mengisikan nilai mean atau modus data atribut ke missing value. Berdasarkan hasil uji coba, performa klasifikasi setelah dilakukan imputasi dengan algoritma MNN mengungguli performa klasifikasi dengan metode imputasi konvensional.

Keywords


Imputasi; klasifikasi; missing value; MNN

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