FAUZAN, AKMAL (2023) ANALISIS POLA PEMBELIAN VOUCHER INTERNET MENGGUNAKAN ALGORITMA APRIORI DAN FP-TREE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Alby Cell counter is a counter that provides internet vouchers. The amount of sales transaction data at the Alby Cell Counter is currently only used to make sales and stock items. The data only serves as an archive, so that sales at Alby Cell have decreased because it cannot predict the data to be processed as a decision maker. Rules that state the association between several attributes are often called affinity analysis or market basket analysis. Association analysis or association rule mining is a data mining technique for finding the rules for a combination of items. FP-Tree is a compressed data storage structure. FP-Tree is built by mapping each transaction data into each specific path in FP-Tree. The results of analysis and testing of internet voucher sales transactions using data mining with the a priori algorithm from 25 product data, 9 transactions from December 1 2020 – April 15 2021 produced 2071 data, resulting in a minimum support value = 20%, minimum confidence value = 80% and product combination patterns and rules by 100%. Furthermore, it is equipped with the FP-Tree algorithm to produce 9 best seller products through the fiterization stage and find patterns of product combinations. So that these 2 methods are very important in making decisions that are useful for preparing what type of stock items are needed in the future. Keywords: Apriori, Association Rules, Data Mining, FP-Tree, Market Basket Analysis Konter Alby Cell merupakan sebuah konter yang menyediakan voucher internet. Banyaknya data transaksi penjualan di Konter Alby Cell saat ini hanya digunakan untuk membuat penjualan dan stok barang. Data tersebut hanya berfungsi sebagai arsip, sehingga penjualan pada Alby Cell mengalami penurunan karena tidak dapat memprediksi data untuk diolah guna sebagai pengambil keputusan. Aturan yang menyatakan asosiasi antara beberapa atribut sering disebut affinity alanysis atau market basket analysis. Analisis asosiasi atau association rule mining adalah teknik data mining untuk menemukan aturan suatu kombinasi item. FP-Tree merupakan struktur penyimpanan data yang dimampatkan. FP-Tree dibangun dengan memetakan setiap data transaksi ke dalam setiap lintasan tertentu dalam FP-Tree. Hasil analisa dan pengujian pada transaksi penjualan voucher internet menggunakan data mining dengan algoritma apriori dari 25 data produk, 9 transaksi dari tanggal 1 Desember 2020 – 15 April 2021 menghasilkan data berjumlah 2071, menghasilkan nilai minimum support = 20%, nilai minimum confidance = 80% dan pola kombinasi produk dan rules sebesar 100%. Selanjutnya dilengkapi dengan algoritma FP-Tree menghasilkan 9 produk best seller melalui tahap fiterisasi dan menemukan pola kombinasi produk. Sehingga dari 2 metode terseberut sangat penting dalam pengambilan keputusan yang berguna untuk mempersiapkan jenis stok barang apa yang diperlukan kedepannya. Kata Kunci : Apriori, Association Rules, Data Mining, FP-Tree, Market Basket Analysis
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