PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS

  • Fitria Febrianti UIN Sunan Ampel Surabaya
  • Moh. Hafiyusholeh UIN Sunan Ampel Surabaya
  • Ahmad Hanif Asyhar UIN Sunan Ampel Surabaya

Abstract

Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data and 70 checking data with RMSE value 2,2122E-14. This indicates that the Fuzzy C-Means method has a higher level of accuracy than the K-Means method

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Published
2016-10-30
How to Cite
FEBRIANTI, Fitria; HAFIYUSHOLEH, Moh.; ASYHAR, Ahmad Hanif. PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS. Jurnal Matematika MANTIK, [S.l.], v. 2, n. 1, p. 7-13, oct. 2016. ISSN 2527-3167. Available at: <http://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/63>. Date accessed: 17 aug. 2017.

Keywords

Iris Data; Fuzzy Logic; Fuzzy C-Means; Mining Data; k-means