Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

Khairul Umam Syaliman, M. Zulfahmi, Aldi Abdillah Nababan

Abstract


Teknik Clustering terbukti dapat meningkatkan akurasi dalam melakukan klasifikasi, terutama pada algoritma K-Nearest Neighbor (K-NN). Setiap data dari setiap kelas akan membentuk K cluster yang kemudian nilai centroid akhir dari setiap cluster pada setiap kelas data tersebut akan dijadikan data acuan untuk melakukan proses klasifikasi menggunakan algoritma K-NN. Namun kendala dari banyaknya teknik clustering adalah biaya komputasi yang mahal, Rapid Centroid Estimation (RCE) dan K-Means termasuk kedalam teknik clustering dengan biaya komputasi yang murah. Untuk melihat manakah dari kedua algoritma ini (RCE dan K-Means) yang lebih baik memberikan peningkatan akurasi pada algoritma K-NN maka, pada penelitian ini akan mencoba untuk membandingkan kedua algoritma tersebut. Hasil dari penelitian ini adalah gabungan RCE—K-NN memberikan hasil akurasi yang lebih baik dari K-Means—K-NN pada data set iris dan wine. Namun dalam perubahan nilai akurasi RCE—K-NN lebih stabil hanya pada data set iris. Sedangkan pada data set wine, K-Means—K-NN terlihat mendapati perubahan akurasi yang lebih stabil dibandingkan RCE—K-NN.

Keywords


Akurasi; Clustering; K-Means; K-Nearest Neighbor (K-NN); Rapid Entimation Centroid (RCE).

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References


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