INDONESIA Pola Asosiasi Untuk Rekomendasi Penataan Display Barang Menggunakan Algoritma Apriori dan FP-Growth (Study Kasus Gamefantasia Ada Swalayan Pati) - BEKRI MURDIANTO; Arief Jananto (STEKOM PRESS, 2023)
This data mining association processes 1224 Gamefantasia ticket redemption transaction data. The goal is to find a pattern of association between goods as a recommendation for structuring the display of goods at the cashier counter and increasing ticket exchange transactions. Modeling uses a comparison of two algorithms, namely the Apriori algorithm and FP-Growth. The data analysis method with the CRISMP-DM method is then processed by RStudio software. The results of the study with the same parameters support 0.02 and confidence 0.1 FP-Growth algorithm formed 53 rules, the strength of the association rule 6.2%, the accuracy was1245%. Whereas the Apriori algorithm forms only 12 rules, the strength of the association rules is 2.1% and the accuracy is 7.8%. Thus, it can be concluded that the use of the FP-Growth algorithm has better results than the Apriori algorithm because it has the highest accuracy in finding transaction patterns.