Penerapan Algoritma Apriori untuk Optimasi Strategi Penjualan Berdasarkan Analisis Pola Pembelian di Torsa Cafe - Anzas Ibezato Zalukhu; Dewi Sartika; Sri Wahyuni (Bulletin of Information Technology (BIT), 2024)
This study aims to analyze consumer purchasing patterns at Torsa Café using data mining methods with the Apriori algorithm to discover association rules between products that are frequently purchased together. In facing the increasingly competitive business environment in the food and beverage industry, understanding consumer purchasing behavior becomes key to enhancing marketing and operational strategies. This research uses sales transaction data from October 2024, consisting of 31 transactions with a total of 129 items. The analysis process begins with data collection and normalization of transaction data, followed by the application of the Apriori algorithm to calculate the support and confidence values of items in the transactions. The analysis results show several items with high support levels, such as "Sanger Espresso", "Avocado Cappuccino Torsa", and "Kopi Susu Torsa", with support values above 30%. Additionally, product combinations frequently purchased together, such as Kopi Tancap with Redvelvet, Macchiato, Frappucino, and Kopi Susu Torsa, can serve as the basis for promotions or more efficient stock management. These findings provide valuable insights for Torsa Café management to determine product placement strategies, raw material stock management, and design more targeted promotions based on the identified purchasing patterns. Therefore, the results of this study are expected to improve operational efficiency and enhance Torsa Café’s competitiveness in the increasingly competitive market.