<![CDATA[Ayu Nadi Swalayan is a retail company that produces a lot of every day sales transaction data, and it is stored for years without knowing the benefits and the placement of the goods is still random. From these problems, an effort is needed to process the data so the data is useful in the future. One of the process is using data mining techniques with apriori hybrid algorithm to find association rules for an items combination. Data product sale in a certain period is used to find the association rules. The results of this study are the development of applications that are used to determine consumer spending habits. So that the company can develop a strategy to promote the product sale and close placement for items that are often purchased together. The application testing found the effect of minimum support, minimum confidence on the number of rules, and lift ratio testing. The smaller the minimum support and minimum confidence, the more rules are generated and vice versa. The lift ratio value is directly proportional to the minimum confidence value and inversely proportional to the minimum support value. The higher the minimum confidence value, the higher the lift ratio value and vice versa. The more items in the transaction cause the minimum support threshold to be lowered in order to generate rules for the data analysis process with the hybrid apriori algorithm]]>
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