INDONESIA Pola Asosiasi Untuk Rekomendasi Penataan Display Barang Menggunakan Algoritma Apriori dan FP-Growth (Study Kasus Gamefantasia Ada Swalayan Pati)
Abstract
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.References
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[3] M. Mariko and U. Amikom Yogyakarta, “Perbandingan Algoritma Apriori Dan Algoritma Fp-Growth Untuk Rekomendasi Item Paket Pada Konten Promosi,” 2021.
[4] F. Rahmawati, N. Merlina, N. Mandiri, J. ; Jl, N. Damai, and W. J. Barat, “Metode Data Mining Terhadap Data Penjualan Sparepart Mesin Fotocopy Menggunakan Algoritma Apriori,” 2018.
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[7] dkk. Ni Luh Wiwik Sri Rahayu Ginantra, Data Mining dan Penerapan Algoritma. Medan: Yayasan Kita Menulis, 2021.
Published
2023-07-14
How to Cite
[1]
B. MURDIANTO and Arief Jananto, “INDONESIA Pola Asosiasi Untuk Rekomendasi Penataan Display Barang Menggunakan Algoritma Apriori dan FP-Growth (Study Kasus Gamefantasia Ada Swalayan Pati)”, ELKOM, vol. 16, no. 1, pp. 109-20, Jul. 2023.
Section
Articles
