ANALISIS KELOMPOK DENGAN ALGORITMA FUZZY C-MEANS DAN GUSTAFSON KESSEL CLUSTERING PADA INDEKS LQ45 - Lailly Rahmatika; Suparti Suparti; Diah Safitri (Jurnal Gaussian, 2015)
Clustering analysis is a data analysis aimed at determining a group of data based on common characteristics. Grouping method that’s being developed now is fuzzy clustering analysis. Fuzzy clustering algorithm that’s commonly used is the Fuzzy C-Means (FCM) algorithm and developed further by Gustafson Kessel Clustering (GK) which is able to detect groups with different shape than the FCM. This study examines the comparative application of FCM and GK clustering method in a case study, namely grouping in LQ45 based on the shares ratio of Earning Per Share (EPS) and Price Earning Ratio (PER). Determination of the optimal number of groups is done through calculation Xie and Beni validity index.In this research the algorithm FCM and GK will be made using MATLAB software, such as  GUI-based application program which can help users to perform clustering analysis. In some cases, the research results showed that GK is better than FCM, specifically in  generating the objective function and the standard deviation ratio of the minimum group. Based on the validity index Xie and Beni, it can be concluded that the optimal number of groups are divided into three.Keywords: Categories of Stocks, Fuzzy C-Means, Gustafson Kessel clustering, Xie and Beni index.