Implementasi Algoritma Equivalence Class Transformation (Eclat) Pada Penjualan Produk Kimia Farma Trading & Distribution Cabang Semarang
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Khumairoh, Ihzanatul
Sudirman, M. Yoga Distra
Luqman, Luqman
Sudirman, M. Yoga Distra
Luqman, Luqman
Subject
Teknik Informatika
Datestamp
2022-09-26 07:46:34
Abstract :
Product inventory is one of the important elements that must be considered in the scope of sales, either goods or services. Because the higher the profit the company will get depends on the number of products sold in a transaction. Therefore the company is emphasized to search for the buying patterns of customers who buy products simultaneously in one transaction. The amount of transaction data that occurs every day requires a long time to determine the buying patterns of customers who buy products simultaneously. Frequent Pattern Mining aims to find item association relationships from the database. In this study, an application was made in which there is an implementation of the method Association rule with theAlgorithm Equivalence Class Transformation (Eclat) so that the time used to determine the buying patterns of customers who buy products simultaneously is faster and more efficient. The algorithm Eclat uses a database with a vertical model. Each item is stored together in the TID List and uses an intersection based approach to calculate the support for an itemset. The data used in this research is product sales transaction data obtained from Kimia Farma Trading & Distribution. From the results of the experiment using a sample data of 57 processed transactions showing the rule rules (Miconazole 2% Cr (Box 24 Tube @ 10 Gram), Iodine Povidone Lar 10% (Btl 60 Ml), Calcium Lactat 500 Mg (Box 100 Tab) , Iodine Povidone Lar 10% (Btl 30 Ml)) with a minimumvalue of support 0.03509, drinking confidence 1 and lift ratio of 7.125.
Product inventory is one of the important elements that must be considered in the scope of sales, either goods or services. Because the higher the profit the company will get depends on the number of products sold in a transaction. Therefore the company is emphasized to search for the buying patterns of customers who buy products simultaneously in one transaction. The amount of transaction data that occurs every day requires a long time to determine the buying patterns of customers who buy products simultaneously. Frequent Pattern Mining aims to find item association relationships from the database. In this study, an application was made in which there is an implementation of the method Association rule with theAlgorithm Equivalence Class Transformation (Eclat) so that the time used to determine the buying patterns of customers who buy products simultaneously is faster and more efficient. The algorithm Eclat uses a database with a vertical model. Each item is stored together in the TID List and uses an intersection based approach to calculate the support for an itemset. The data used in this research is product sales transaction data obtained from Kimia Farma Trading & Distribution. From the results of the experiment using a sample data of 57 processed transactions showing the rule rules (Miconazole 2% Cr (Box 24 Tube @ 10 Gram), Iodine Povidone Lar 10% (Btl 60 Ml), Calcium Lactat 500 Mg (Box 100 Tab) , Iodine Povidone Lar 10% (Btl 30 Ml)) with a minimumvalue of support 0.03509, drinking confidence 1 and lift ratio of 7.125.
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