Application Of Fuzzy C-Means Clustering for Mapping Agent

Abstract

Strict business competition in the field of mountain equipment providers and selling the same product makes the mapping of onsight agents needed to determine the priority of agents prioritized. Fuzzy C-means is one of the data grouping techniques in which the existence of each data point in a cluster is determined by the level of membership. The purpose of this study is to design and make applications for grouping agents. The research method used is direct interview to obtain information in the form of ordered item data. The design model uses the System Development Life Cycle (SDLC). The system design method used is the Unified Modeling Language (UML). Agent mapping system with web-based fuzzy c-means clustering uses the PHP and MySQL programming languages as the database. The results of this study are in the form of three data clusters that can be used to support decisions for priority and from 30 data agents, the first cluster consists of 15 agents, the second cluster consists of 1 agent, and the third cluster consists of 14 agents