Pencarian Frequent Itemset dengan Algoritma Apriori dan Python. Studi kasus: Data Transaksi Penjualan Eceran Online di UK
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Abstract
Saat ini, sejumlah besar data disimpan dalam basis data di berbagai sektor bisnis. Dengan data mining kita dapat mengekstrasi informasi yang berguna dari data ini. Salah satu teknik data mining yaitu Market Based Analysis digunakan untuk menemukan aturan asosiasi di antara kumpulan data. Dalam penelitian ini data yang digunakan adalah data publik transaksi online eceren di UK. Dari hasil penelitian didapatkan 11 itemset dengan aturan minimum support 1.5% dan confidence ≥ 80%, dan 3 itemset dengan aturan minimum support 2% dan confidence ≥ 80%. Hasil ini dapat digunakan kepentingan bisnis di online retail untuk merekomendasikan produk yang sering dibeli bersamaan. Implementasi dilakukan menggunakan python menggunakan pustaka mlxtend, pandas dan sys. Hasil ini adalah itemset yang sering dibeli dari total 22.846 transaksi dan 4.055 produk.
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