Market Basket Analysis to Determine Muslim Clothing Supply in Indonesia Ahead of Eid Al-Fitr - Indra Gunawan, Gun Gun; Aji, Tri Wahyu; Juliane, Christina (Forum Kerjasama Pendidikan Tinggi, 2024)
Enterprise transaction data is a valuable source of insights for companies to increase sales. In preparation for Eid al-Fitr, this study leverages Market Basket Analysis with the FP-Growth algorithm to uncover buying patterns within Indonesia's Muslim clothing market. Market Basket Analysis is one way to explore information through data to find customer buying patterns that are often used as insight into company decision-making. The data processing method uses the FP-Growth algorithm, which generates association rules based on calculating the frequency of occurrence of itemsets. Using the FP-Growth algorithm gives good results in the determination of association rules. From Muslim fashion store transaction data over the last 12 months, it produced 30 item set patterns with a minimum support value of 0.009 and confidence of 0.58. By identifying these in-demand product pairings, businesses can make informed decisions about stock allocation. This ensures they have the right combination of items available to meet customer needs during the surge in demand leading up to Eid al-Fitr. Additionally, these patterns can inform targeted promotional campaigns and strategic bundling initiatives, maximizing sales and customer satisfaction throughout this critical sales period.