Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset

Authors

DOI:

https://doi.org/10.33633/tc.v20i1.4230

Keywords:

Data mining, initial centroid, k-means, Sum Square Error, fitness, Davies Bouldin Index, GA, DAC-GA, Tuberculosis

Abstract

Data merupakan hal penting diera sekarang begitu  juga dengan metode data mining yang dapat mengekstraksi data menghasilkan informasi. Klastering  1 dari 5 peran data mining yang berfungsi untuk mengelompokkan data berdasarkan tingkat kemiripan dan jarak minimum. Algoritma K-Means  termasuk algoritma yang populer banyak digunakan diberbagai bidang seperti bidang pendidikan, kesehatan, sosial, biologi, ilmu komputer. Seringkali metode K-Means dikombinasikan dengan metode optimasi seperti algoritma genetika untuk mengatasi permasalah pada K-Means yaitu sensitif dalam penentuan centroid awal .Namun metode algoritma genetika memiliki kekurangan yaitu mengalamai konvergen prematur sehingga hasil dari algorima genetika terjebak pada optimum lokal. Penelitian ini mengkombinasikan dynamic artificial cromosomes genetic algorithm dengan K-Means dalam menentukan nilai centroid awal pada k-means. Hasil eksperimen menunjukkan bahwa metode DAC GA + K-Means lebih unggul dibandingkan dengan K-Means dan GA + K-Means pada 2 dataset yang diuji dengan optimal nilai klaster sebanyak 2 dan 1 dataset sebanyak 3 klaster. Metode tersebut perolehan nilai DBI sebesar 0.138, 0.279 serta 0.382, nilai Sum Square Error sebesar 92.56, 332,39 dan 1280.68 serta nilai fitness yang tebentuk adalah 7.12, 3.57 dan 2.13.

Author Biography

Mursalim Mursalim, Universitas Dian Nuswantoro

Master Student

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Published

2021-02-09

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Vol. 20 No. 1 (2021): Februari 2021

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Copyright (c) 2021 Mursalim Mursalim, Purwanto Purwanto, M Arief Soeleman

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