Customer Segmentation Based on RFM Value on the Sale of Electronic Kopmen BMI Using K-Means Clustering Algorithm

Abstract

At present, the development of information technology is increasing rapidly. The need for information and data processing in various aspects of human life is critical, as well as customer data processing in BMI Consumer Cooperatives. This situation can impact information providers in an organization or company that requires fast, precise, and accurate data processing. The customer segmentation clustering system at the BMI Consumer Cooperative has yet to be implemented. A K-means clustering system is needed to increase customer loyalty, which can simplify the process of grouping customer segmentation. In this thesis, researchers use a descriptive method as a research methodology, which is used to get an overview and explanation of the state of the research object based on facts. As for the data collection method, researchers used interviews, observation, and literature study. In developing the system, researchers use prototyping. The customer segmentation information system application prototype describes the implementation of Astah's UML (Unified Modeling Language) and program planning used by Python. The conclusion of this prototype application can make it easier for managers to get information about customer segmentation data.