Implementasi Data Mining Association Rules Menggunakan Algoritma Fp-Growth untuk Data Penjualan Keramik

Abstract

The ceramic company CV Sukses Bersama is facing challenges in determining the optimal product layout and promotion strategy. To address this issue, this research applies the Data Mining Association Rules method using the FP-Growth algorithm. With the Python programming language, the author conducts an analysis of the company's sales data to identify significant purchasing patterns. The analysis results reveal that the product 'MCC' enjoys an exceptionally high level of popularity, with a support rate reaching 94.86%. This indicates that 'MCC' is the primary favorite among CV Sukses Bersama's customers. The analysis also unveils several significant Association Rules, such as {'MCC'} -> {'HRM'} with a confidence level of 86.99%. This implies that customers who purchase 'MCC' tend to buy 'HRM' with a high level of certainty. These findings hold strategic importance for CV Sukses Bersama, offering valuable insights that can be utilized to design more effective marketing strategies by understanding customer preferences and optimizing product stock management.