Abstract:
The amount of compettion, especially in the daily sales, requires the owners of shop to
find strategies to increase daily sales, namely by knowing the pattern of daily sales so
that owners can implement appropriate steps to increase selling power. Sales
transactions can be used to determine the pattern of daily sales. Sales patterns can be
processed into information by applying data mining methods with association rule
techniques with a priori algorithms. Processing of daily goods sales transaction data of
7092 transaction data with a minimum support of 20 and a minimum of 50% confidence
resulted in 32 association rules. In the lift ratio test, there are 12 association rules with a
value of more than 1, meaning that there is a dependency between the antecedent and the
consequent so that the rule can be used as a prediction of the appearance of a daily item
due to the appearance of other daily items and can be used as a reference in
recommending daily items.
