Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT22868 104 Pattern Recognition of Customer Spending Habits Using Apriori Algorithms in DataMining as an Inventory Strategy Arief Jananto 1 , Yohanes Suhari 2 , Rara Sriartati Redjeki 3 , Bambang Sudiyatno 4 1,2,3 Information Systems Department, Faculty of Information Technology and Industry, Stikubank University, Semarang, Indonesia * 4 Department of Management, Fakulty of Economics and Business, Stikubank University, Semarang, Indonesia Article Info Publication Issue : Volume 8, Issue 6 November-December-2022 Page Number : 104-115 Article History Accepted: 06 Nov 2022 Published: 12 Nov 2022 ABSTRACT The readiness of product inventory is very important, product shortages related to other products can make buyers disappointed and then cancel to buy products that were previously planned to be purchased at once. Sellers can experience a decrease in the number of sales to revenue. In this case, the seller needs to know the pattern of customer habits when making purchases by going through sales transaction data that has occurred. Association techniques can be used to analyze the pattern of interrelationships between items in transaction events. With the a priori algorithm as a popular association algorithm, the pattern of sales transaction data can be analyzed through the research stage. From the implementation of the algorithm with 1063 transaction data using 10% min- support and 75% min-confidence resulting in 4 association rules where 1) if you buy "kacer" and "love bird" you will buy "pentet" as much as 17% support, 2) if you buy "magpie" and "love bird" will also buy "pentet" at 16%, 3) if you buy "kacer" and "magpie" then you will buy "pentet" at 14%, 4) if you buy "anis" you will buy "pentet" of 11% with a confidence level of 76%, 81%, 84%, 77%, respectively. So, there are 5 main items that play a strong role in the rule that must be considered. Sellers can use the resulting item relationship patterns as consideration in managing inventory and structuring the items sold. Keywords: Apriori, Inventory, Association Rules, Data Mining I. INTRODUCTION The readiness of product inventory to be marketed is very important. Sometimes there is a vacancy of a product related to another product that is desired by the buyer, which can make the buyer disappointed and then cancel to buy the product that was previously planned to be purchased at once. In fact, eventually move to another seller who is more complete. As a result, the seller will experience a decrease in the number of sales which of course has an impact on a decrease in revenue. The amount of availability of goods stored in the warehouse is very important because management can determine the minimum and maximum stock [1]. This means that there are still many sellers who do not or do not