<![CDATA[This study aims to identify association patterns in the use of online food delivery applications by applying the FP-Growth algorithm. This research is important as it helps service providers understand consumer preferences and develop more effective promotional strategies. The method used is a quantitative data mining approach, analyzing 1,000 transactions using the FP-Growth algorithm implemented in Python. The results indicate that certain item combinations—such as main dishes and sweet beverages—are frequently ordered together, alongside a notable trend of multi-platform usage among consumers. The values of support, confidence, and lift reveal strong relationships between items. It is concluded that the FP-Growth algorithm is effective in identifying consumption patterns efficiently, and its findings can be utilized to develop menu recommendation systems or data-driven promotional strategies. Further research is recommended to incorporate time and location variables for more contextual analysis]]>
Copyrights © 2025
