IMPROVEMENT MARKET BASKET ON TRANSACTION SALES USING APRIORI ALGORITHM ( Case study : Workshop The Sari (Mulya )

Abstrak

This research refers to the improvement of Market Basket with Apriori Algorithm to analyze Bengkel Las Sari Mulya data. At the implementation stage, Apriori Algorithm is used to find unique values from the data carefully and accompanied by UAT (User Acceptance Test ) testing for tests that can be used as evidence that the software has been accepted with an average value of 4.7 that this application is very satisfying for users and has met the needs of those who request it. To find out the response of the Workshop owner to the application. The results of the analysis show an association between Goods such as Fences and Canopies, by determining a Support value of 15% and Confidence of 20%, resulting in an association rule value with a Support value of 24% and Confidence of 73%. In conclusion, customers who buy fence goods tend to also buy Canopies in one transaction. These results can be used for marketing strategies and stocking goods, for example, with frequently sold goods such as fences and canopies, you can stock goods such as: 2x4 Galvanized Iron, 4x4 Galvanized Iron, 4x8 Galvanized Iron, 5x10 Galvanized Iron, spandek, Alderon, Because from these results, fences and canopies can be found, goods that are often sold and it can be seen that both use the materials used in making goods, the owner will easily make decisions regarding the plan each month.

Say Key: Apriori Algorithm, Transaction Data, Welding Workshop.