Penerapan Data Mining Metode Apriori Dan FP-Tree pada Penjualan Media Edukasi (Studi Kasus : Oisha Smartkids)

IJCIT - Indonesian Journal on Computer and Information Technology

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Title Penerapan Data Mining Metode Apriori Dan FP-Tree pada Penjualan Media Edukasi (Studi Kasus : Oisha Smartkids)
 
Creator Junianto, Erfian
Rachman, Rizal
 
Subject apriori; association rules; data mining; fp-tree; market basket analysis
 
Description Selama ini Oisha Smartkids telah melayani sekian banyak transaksi pesanan produk–produk media edukasi. Setiap data transaksi tersebut disimpan di dalam suatu sistem basis data melalui aplikasi sistem informasi manajemen. Seiring meningkatnya dunia toko online maka informasi mengenai produk-produknya menjadi kebutuhan. Salah satu yang menjadi kebutuhan penting yaitu informasi mengenai penjualan dan persediaan produk media edukasi. Algoritma Apriori termasuk jenis aturan asosiasi pada data mining. Aturan yang menyatakan asosiasi antara beberapa atribut sering disebut affinity analysis 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 media edukasi menggunakan data mining dengan algoritma apriori dari 30 data produk, 12 transaksi setiap bulannya selama tahun 2019 menghasilkan nilai minimum support = 25%, nilai minimum confidence 90% dan pola kombinasi produk dan rules sebesar 100%.  Selanjutnya dilengkapi dengan algortma FP-tree menghasilkan 10 produk best seller melalui tahap filterisasi dan menemukan pola kombinasi produk. Sehingga dari 2 metode tersebut sangat penting dalam pengambilan keputusan yang berguna untuk mempersiapkan jenis stok barang apa yang diperlukan kedepanya.So far, Oisha Smartkids has served many transactions for orders for educational media products. Each transaction data is stored in a database system through a management information system application. As the world of online stores increases, information about its products becomes a necessity. One of the important needs is information about sales and inventory of educational media products. Apriori algorithm including the type of association rules in data mining. 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 of a combination of items. And FP-Tree is a compressed data storage structure. FP-tree is built by mapping each transaction data into each particular path in FP-tree. analysis and testing results on educational media sales transactions using data mining with a priori algorithm of 30 product data, 12 transactions per month during 2019 resulting in a minimum support value = 25%, a minimum confidence value of 90% and a combination of product and rules pattern of 100%. Furthermore, equipped with FP-tree algortma produces 10 best seller products through the filtering stage and finding patterns of product combinations. So from the 2 methods are very important in making decisions that are useful for preparing what types of goods needed in the future.
 
Publisher LPPM Universitas Bina Sarana Informatika
 
Contributor
 
Date 2020-11-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artikel yang dipeer-review
 
Format application/pdf
 
Identifier https://ejournal.bsi.ac.id/ejurnal/index.php/ijcit/article/view/8308
10.31294/ijcit.v5i2.8308
 
Source IJCIT; Vol 5, No 2 (2020): November 2020
IJCIT (Indonesian Journal on Computer and Information Technology); Vol 5, No 2 (2020): November 2020
2549-7421
2527-449X
10.31294/ijcit.v5i2
 
Language ind
 
Relation https://ejournal.bsi.ac.id/ejurnal/index.php/ijcit/article/view/8308/pdf
 
Rights ##submission.copyrightStatement##
http://creativecommons.org/licenses/by/4.0
 

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