Implementasi Algoritma FP-Growth untuk Optimalisasi Strategi Pemasaran di Toko Pakaian: Studi Kasus Toko Trend - Batara Mahardika Aryoko; Saiful Nur Budiman; Sri Lestanti (Jurnal Teknologi Terpadu, 2026)
TREND store is a retail outlet that sells various types of clothing and accessories. As market competition intensifies, the store needs to develop more efficient marketing strategies to remain competitive. One approach is to utilise sales data to analyse consumer purchasing patterns, given that such data has not been optimally used previously. This study aims to identify purchasing patterns and generate association rules as a basis for marketing strategies using the FP-Growth algorithm. The algorithm was chosen because it can identify frequent itemsets without candidate generation, making it more efficient than other methods in market basket analysis. The research data consist of 64 sales transactions from March 2025. In addition to pattern discovery, lift ratios were calculated to measure the strength of relationships between items. The results show that FP-Growth successfully identified significant purchasing patterns and generated relevant association rules. Several rules have lift ratios above 1, such as 1.2472 and 1.1463 for the combination K7, K1, C5, indicating positive relationships. These findings can be used to develop more data-driven and efficient marketing strategies, such as placing related items together to encourage impulsive purchases, supporting product recommendations, promoting planning, and informing other marketing decisions.