Application of the Apriori Algorithm in Supporting Sales Strategies at Yolanda Bakery
Keywords:
Apriori; Product Association; Purchase Patterns; Transaction Data; Sales StrategyAbstract
Yolanda Bakery is a small-scale bakery business that sells various types of bread, cakes, and other flour-based products, generating daily transaction data that can potentially be used to analyze consumer purchasing patterns. However, this data has not been optimally processed, so sales strategies, display arrangements, and promotional planning still rely on intuition rather than accurate information. To address this issue, this study aims to identify associations between products that are frequently purchased together using the Apriori Algorithm as an analytical approach. The dataset used consists of a collection of daily transactions that then undergo preprocessing and are converted into binary format to suit pattern analysis. The frequent itemset results show that several products, such as Bread (52%), Cakes (28%), and Medialuna (28%), have a relatively high occurrence rate compared to other items. In the association rule formation stage, significant patterns were found, such as Medialuna ? Bread and Cake ? Bread, with support values of 12% and confidence values of 44%, respectively, indicating a tendency to purchase products together even though the lift value was in the range of 0.85. Heatmap visualization then clarified the relationship between products by displaying combinations of items that appeared more frequently together in transactions. The results of this analysis can be implemented in the form of bundling strategies, display rearrangement, and promotion optimization to increase marketing effectiveness and support more accurate decision-making at Yolanda Bakery.
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