COMPARISON STUDY OF FUZZY C-MEANS AND FUZZY SUBTRACTIVE CLUSTERING IMPLEMENTATION IN QUALITY OF INDIHOME FIBER OPTIC NETWORK (Case Study in PT. TELKOM INDONESIA)

Abstract

This research is conducted for grouping or clustering the quality assessment rule of IndiHome fiber optic cable network using fuzzy clustering method in PT Telkom company and to understand the difference type of clustering by observing the mapping and clustering data results that presented by each algorithm method of Fuzzy Subtractive Clustering and Fuzzy C-Means Clustering results. It applied ten predictor variables that affect the quality of the system through the study of previous research literature. Several factors that affect the transmission are Tx Power, Rx Power, Temperature, Power Supply, and Bias Current. Later, cluster validation is performed by using Partition Coefficient Index (PCI) and Partition Coefficient Index (PEI) indicator. This research uses the Fuzzy Subtractive Clustering process with cluster radius is from 0.1 until 1. Each radius has each number of clusters, nevertheless, for radius 0.1 the number of clusters that formed are 4, while radius 0.2 to 1, there is only one cluster formed. In Fuzzy Subtractive Clustering, it is considering some of the parameter which are the accept ratio 0.5, the reject ratio 0.15, and squash factor 1.25. In Fuzzy C-Means result, the value of the PCI (Partition Coefficient Index) is 0.662786731. Then, the value of the PEI (Partition Entropy Index) is 0.546967522. From the results of Fuzzy Subtractive Clustering, highest value of PCI are resulted in radius 1 with the value of 0.451738. The smallest PEI is in radius 0.2 with the value of 0. 0.070139. Then, it can be stated that both methods are better within each parameter. But after considering the number of clusters that are formed, compared to fuzzy c-means method has 4 clusters and in fuzzy subtractive only two clustering numbers are formed, which are 41 and 1. In conclusion, the method that will be preferred in terms of grouping quality is Fuzzy C-Means.