Aplikasi Sequential Pattern Discovery Using Equivalence Classes (Spade) Pada Data Transaksi Penjualan (Studi Kasus Produk Kecantikan Dan Perawatan Tubuh).

Content Provider	Semantic Scholar
Author	Shalihah, Anisah Mufidatush
Copyright Year	2016
Abstract	ANISAH MUFIDATUSH SHALIHAH. Application of Sequential Pattern Discovery using Equivalence Classes (SPADE) on Sales Transaction Data (Case Study for Beauty and Body Treatment Product). Supervised by AGUS MOHAMAD SOLEH and LA ODE ABDUL RAHMAN. Every retail store has a big transaction data which is collected everyday. It can be used to mining many informations, such as consumer shopping pattern. Consumer shopping pattern will be useful for marketing strategy making. The analysis that commonly used is association rule mining. In order to mine better pattern, this research use sequential pattern mining using SPADE algorithm. This research use real transaction data at beauty and treatment retail store from January 1 until April 22 2015. This research use 0.01% minimum support and 1% minimum confidence, both of these values were determined based on the data condition. Based on the results of this research, the items that costumer likely to buy are teatree facewash and teatree oil. The groups that costumer likely to buy are lip make up and base make up. For the training and test data that have a balance length of selling period and transactions, the longer selling period and the more transaction, the better prediction can be made.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://repository.ipb.ac.id/bitstream/handle/123456789/85770/G16ams.pdf?isAllowed=y&sequence=1
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article