Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering   dengan Menggunakan Algoritma K-Means

Muhammad Sholeh; Khurotul Aeni

doi:10.30998/string.v8i1.16388

Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means

Muhammad Sholeh^(1*), Khurotul Aeni⁽²⁾

(1)
(2) Universitas Peradaban
(*) Corresponding Author

Abstract

One of data mining model that is often used is the clustering model. The clustering model is used to create a grouping of a datasheet. Data clustering can be used to distinguish data in a datasheet. Many of the best groupings can be done with the clustering model evaluation process. In the clustering method, the evaluation process can use various evaluation methods. The research will cluster the results of a survey on reviews of tourist destinations consisting of 10 categories. The method used is a data mining method, namely Knowledge Discovery in Database (KDD). Stages in KDD include data selection, data cleaning, transformation, data mining process and evaluation of model results. The process of creating a clustering model uses a public datasheet, namely tripadvisor_review.csv. The data clustering process uses the K-means algorithm. The result of the clustering will be tested by comparing evaluation methods. This evaluation method is used to select the best amount of clustering. Testing to get the best clustering results is conducted by testing from clustering 2 to 15. The evaluation uses Davies Bouldin, Elbow and Silhouette methods. The result shows that the number of datasheet groupings with the three evaluation methods provides recommendations for grouping as many as 2 groups.

Keywords

Clustering; evaluation; Davies Bouldin; Elbow; Silhouette

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DOI: http://dx.doi.org/10.30998/string.v8i1.16388