Analisis Pola Asosiasi Penggunaan Aplikasi Pemesanan Makanan Online Menggunakan Algoritma Fp-Growth
DOI:
https://doi.org/10.54951/sintama.v5i3.1047Keywords:
Association Pattern, FP-Growth Algorithm, Data Mining, Online Food OrderingAbstract
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
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Copyright (c) 2025 Arina Nindya Sari, Dwita Amalia Rizki, Rakha Adhi Nugraha
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