UTILIZING FP-TREE AND FP-GROWTH ALGORITHMS FOR DATA MINING ON MEDICINE SALES TRANSACTIONS AT KHANINA’S

RIZALDI ARDIANSYAH, NPM 2009100051 (2024) UTILIZING FP-TREE AND FP-GROWTH ALGORITHMS FOR DATA MINING ON MEDICINE SALES TRANSACTIONS AT KHANINA’S. Tugas_Akhir (Artikel) INFORMATIKA Universitas Labuhanbatu, 12 (3). pp. 404-416. ISSN 2615-1855 (e-ISSN)/ 2303-2863 (p-ISSN)

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Abstract

Although Khanina Pharmacy is a growing pharmacy with a lot of processes, the data processing is still done by hand. This study examines the use of the FP-Tree and FP Growth algorithms to the medication sales transaction system. The FP-Tree and FP Growth algorithm methods use methods or strategies to choose data in order to identify trends or intriguing details. The FP-Tree and FP-Growth algorithm approaches are two frequently used techniques in data mining. The purpose of this medicine sales transaction data is to identify concurrently purchased products. The FP-Growth Algorithm is used to find item pattern combinations. Use of FP-Tree to identify frequently occurring itemsets from a database in combination with the FP-Growth algorithm. When searching for product attachment patterns for sales tactics in decision making rules, the Association Rule method is employed. In order to determine which medications are frequently bought by customers, we can create rules using the data in the database. The Rapidminer 5 program was used to conduct the test. This test yielded the following results: the number of itemsets created and rules constructed increased with decreasing support values. Keywords : Method FP-Growth, FP-Tree, Rule, Rapidminer 5.

Item Type:	Article
Uncontrolled Keywords:	Method FP-Growth, FP-Tree, Rule, Rapidminer 5.
Subjects:	Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	24 Sep 2024 03:46
Last Modified:	24 Sep 2024 03:46
URI:	http://repository.ulb.ac.id/id/eprint/1149

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