Skripsi

PENENTUAN POLA PEMBELIAN KONSUMEN PADA TOKO MIRING MENGGUNAKAN ALGORITMA FP-GROWTH.

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Penilaian

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One of the important issues that has been the focus of public attention lately is the fulfillment of basic needs. People usually buy necessary items through convenience stores to meet those needs. Toko Miring sells everyday items such as beverages, food, health products, beauty products, home, and living essentials. Certain combinations of items often appear in many transactions, forming a pattern or interconnected combination of items. This study aims to apply the FP-Growth algorithm to identify frequently purchased combinations of items by customers, thus determining consumer purchasing patterns through the generated association rules. The results of these association rules can be used as references in marketing and sales of goods. This study uses 12,000 sales transaction data and generates 34 association rules with a minimum support criterion of 0.25% and a minimum confidence criterion of 50%. One of the obtained rules is that customers purchase INDOMIE KARI AYAM 72G and then purchase INDOMIE GORENG 80G, with a support value of 1.44% and a confidence value of 61.9%, as well as a lift ratio of 12.53. The results of this research also indicate that the FP-Growth algorithm has better computational speed compared to other algorithms due to the pruning process, which reduces the number of processed combination patterns, and the use of the FP-Tree structure, which reduces the amount of data scanning.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002473	T113155	T1131552023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1131552023

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 66 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.107

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Identifikasi Sistem

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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