Penerapan algoritma fp-growth untuk optimasi menu dan strategi rekomendasi di bricks brew & food

Arrafi, Muhammad Atha (2026) Penerapan algoritma fp-growth untuk optimasi menu dan strategi rekomendasi di bricks brew & food. Bachelor thesis, Institut Teknologi Kalimantan.

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Abstract

Ketatnya persaingan di industri kuliner mengharuskan pelaku usaha memahami perilaku konsumen demi menciptakan strategi pemasaran dan rekomendasi menu yang efektif. Penelitian ini bertujuan mengoptimalkan rekomendasi menu di Bricks Brew & Food dengan menerapkan teknik association rule mining melalui algoritma FP-Growth. Menggunakan data transaksi penjualan digital periode November–Desember 2025, algoritma FP-Growth dipilih karena efisiensinya dalam menemukan frequent itemset tanpa perlu membentuk kandidat itemset seperti pada algoritma Apriori. Analisis ini difokuskan untuk mengidentifikasi kombinasi makanan dan minuman yang sering dibeli bersamaan berdasarkan signifikansi nilai support, confidence, dan lift ratio. Hasil penelitian berhasil mengidentifikasi tiga aturan asosiasi terkuat: (1) kombinasi Kopi Hitam dan Apple Ice terhadap Americano (confidence 85,71%, lift 3,23), yang potensial untuk paket "Coffee Combo Package"; (2) Fried Fries dan Apple Ice terhadap Americano (confidence 80%, lift 3,02) untuk strategi cross-selling "Snack and Coffee Time"; serta (3) Tempe Mendoan dan Apple Ice terhadap Americano (confidence 66,67%, lift 2,51) yang membuka peluang paket menu lokal-modern. Penelitian ini memberikan kontribusi praktis dalam pengelolaan menu berbasis data bagi Bricks Brew & Food sekaligus memperkaya literatur penerapan FP-Growth di sektor kuliner.

Item Type:	Thesis (Bachelor)
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software
Divisions:	Jurusan Matematika dan Teknologi Informasi > Statistik
Depositing User:	Muhammad Atha Arrafi
Date Deposited:	06 Jul 2026 02:55
Last Modified:	06 Jul 2026 02:55
URI:	http://repository.itk.ac.id/id/eprint/25658

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