METODE HIGH ORDER FUZZY TIME SERIES MULTI FACTORS DENGAN ALGORITMA FUZZY C-MEANS - Yuni Wulandari; Dewi Retno Sari Saputro (UNEJ e-Proceeding, 2022)
Clustering is the process of grouping data into several clusters so that the data in a cluster has a high degree of similarity between data with one another but is very different from the data in other clusters. Fuzzy clustering is a technique to determine the optimal cluster in a vector space based on the Euclidian normal form for the distance between vectors. Fuzzy clustering is very useful for fuzzy modeling, especially in identifying fuzzy rules. There are various kinds of fuzzy clustering techniques, one of which is Fuzzy Cluster-Means (FCM). Fuzzy C-Means clustering is a data clustering technique in which the existence of each data point in a cluster is determined by the degree of membership. The purpose of this study is to examine the High Order Fuzzy Time Series Multi Factors method with Fuzzy C-Means in order to get k locations of the data cluster center points as many as k which are then used to form subintervals. The results show that Fuzzy C-Means replaces the process in the High Order Fuzzy Time Series Multi Factors method, which is when the subinterval is formed. Keywords: Clustering, Fuzzy C-Means, Metode High Order Fuzzy Time Series Multi Factors.