ANALISIS PERUBAHAAN PERMINTAAN TRANSAKSI UNTUK MENINGKATKAN KEPUASAAN PELANGGAN SHOPEE DENGAN ALGORITMA FUZZY C MEANS - Tajrin, Tajrin; Hasugian, Debi Maria; Nasution, Olyfia Akbar (Politeknik Bisnis Indonesia, 2024)
This study uses Fuzzy C-Means to analyze Shopee customer demand and transaction success, aiming to improve customer satisfaction by understanding shopping patterns and purchase conversion rates. With the rapid growth of internet usage and e-commerce, consumer behavior analysis has become crucial for improving customer satisfaction. This study utilizes the Fuzzy C-Means algorithm to cluster data based on attributes such as location, product price, sales volume, and customer ratings. The Fuzzy C-Means algorithm allows handling ambiguous data and identifying significant patterns in transactions and customer satisfaction. The study results indicate that the algorithm successfully grouped the data into three main clusters: the first cluster has an average price of Rp 120,000, an average sales volume of 5,000 units, and an average rating of 4.8; the second cluster has an average price of Rp 140,000, an average sales volume of 3,000 units, and an average rating of 4.7; the third cluster has an average price of Rp 130,000, an average sales volume of 4,000 units, and an average rating of 4.9. This research provides valuable insights for e-commerce companies to design more effective marketing strategies and improve service quality based on the analysis of demand changes and transaction conversion effectiveness.