Analisis Kinerja Algoritma Clustering Fuzzy C-Means dengan Fuzzy Tsukamoto

Abstract

Based on the analysis algorithm performance clustering models Tsukamoto Fuzzy and Fuzzy C-Means displays seyeral variables that vary in number. For grouping was done to determine the value of the performance of the algorithm is effective. And therefore, needed a method to facilitate the grouping of employee value. With fuzzy clustering approach, the division of labor based on the target group or the values Main Duties (Tu), Competence (Kom) and Compliance (Kep). In this research clustering process value using Fuz4, C-Means algorithm. By using Fw4t C-Means aims to facilitate categorize a value with the result of the rule Excellent value (1.765), Better (0,900), Good + (0.59), OKay (0.405), Almost Good (0.206) and No Good ( 0.121). From the results obtained both models is to have a good performance and the results are equally equivalents.