ANALISIS KINERJA ALGORITMA CLUSTERING FUZZY TSUKAMOTO DENGAN FUZZY C-MEANS

Abstract

Efforts to evaluate employees in the work is to assess the performance of each employee. For it has been formulated assessment is based upon work objectives according to the position or job title, and by weighting against six indicators into three groups. The number of data values and indicators to be used will certainly lead to difficulties in implementation, not effective and less objective. Therefore we need a clustering process more optimal assessment. This study aims to analyze the performance of FCM algorithm implemented on employee performance evaluation PT. Bank Syariah Mandiri into 3 clusters. Some of the steps that must be performed before clustering, first performed pretreatment, namely data cleaning and data transformation for further clustering using the algorithm. The results of the calculations used to analyze the performance of the algorithm with FCM Tsukamoto. Compatibility calculation value data by Tsukamoto algorithm is pretty good and for the FCM algorithm is Very Good. FCM algorithm can be used in the assessment of grouping data based on the three criteria of assessment.