<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "CUSTOMER BEHAVIOR SEGMENTATION ANALYSIS\r\nIN THE E-COMMERCE INDUSTRY USING THE FUZZY\r\nC-MEANS CLUSTERING METHOD"^^ . "CUSTOMER BEHAVIOR SEGMENTATION ANALYSIS\r\nIN THE E-COMMERCE INDUSTRY USING THE FUZZY\r\nC-MEANS CLUSTERING METHOD\r\nFrans Dito Aryo Wibowo 1)\r\n, Aries Maesya 2)\r\n, Dinar Munggaran Akhmad 3)\r\n1)2)3) Pakuan University, Pakuan University, Pakuan University, Indonesia\r\nAbstract\r\nBackground: Indonesia's increasingly competitive economic conditions require companies to understand customer\r\nbehavior patterns more accurately. In an era of tight business competition, companies are required to compete not only\r\nin products, but also in the ability to analyze customer data to develop long-term business and investment strategies.\r\nHowever, consumer behavior is complex and does not always match their statements or expectations, so a data-based\r\nanalytical approach such as data mining is needed.\r\nObjective: This study aims to analyze customer behavior with a data mining approach to help companies recognize\r\ncustomer purchasing patterns more accurately. With a deeper understanding of consumer behavior, companies can group\r\ncustomers into specific segments and find patterns of relationships between products that are often purchased together.\r\nThe results of this analysis are expected to be used as a basis for developing marketing strategies and making more\r\ntargeted business decisions.\r\nMethods: The methods used in this study are Fuzzy C-Means Clustering (FCM) and FP-Growth. FCM is a fuzzy logicbased clustering technique that allows each data to have a degree of membership in more than one cluster, thus providing\r\nflexibility in customer grouping. While the FP-Growth algorithm is used to find frequent itemsets or item patterns that\r\noften appear together in customer transactions. FP-Growth works by building an FP-Tree data structure, which allows\r\nthe pattern extraction process to be carried out efficiently without having to produce candidate combinations as in the\r\nApriori algorithm.\r\nResults: Through the clustering process with FCM, customers are successfully grouped into several clusters based on\r\nsimilarities in purchasing behavior, such as transaction frequency, purchase amount, shopping value, and product\r\npreferences. Each cluster shows unique characteristics that can be utilized for different marketing approaches. In\r\naddition, the application of the FP-Growth algorithm produces association patterns between products, namely items that\r\nare often purchased together by customers, which can be used as a basis for building a product recommendation system\r\nor bundling offers.\r\nConclusion: The results of the study show that the combination of Fuzzy C-Means and FP-Growth methods can provide\r\na more comprehensive understanding of customer behavior. FCM is effective in grouping customers with a high level\r\nof flexibility, while FP-Growth is able to identify purchasing patterns that are useful for marketing strategies. Both\r\nmethods provide important contributions in supporting smarter, adaptive, and data-driven business decision-making,\r\nespecially in the face of increasingly competitive industry competition.\r\nKeywords: Analysis, Fuzzy C-Means Clustering (FCM), FP-Growth, Segmentation, Clustering."^^ . "2025-02-18" . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan"^^ . . . . . . . . . . . "Aries"^^ . "Maesya"^^ . "Aries Maesya"^^ . . "Frans Dito"^^ . "Ariyo W,"^^ . "Frans Dito Ariyo W,"^^ . . "Dinar"^^ . "Munggaran"^^ . "Dinar Munggaran"^^ . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pnegetahuan Alam"^^ . . . "Program Studi Ilmu Komputer"^^ . . . . . . "HTML Summary of #9680 \n\nCUSTOMER BEHAVIOR SEGMENTATION ANALYSIS \nIN THE E-COMMERCE INDUSTRY USING THE FUZZY \nC-MEANS CLUSTERING METHOD\n\n" . "text/html" . . . "Ilmu Komputer"@en . .