Application of Data Mining Method to Determine Purchasing Patterns Using Apriori Algorithms and Fp-Growth in Mukara Stores

  • Anwan Chailes UNIVERSITAS BUDDHI DHARMA
  • Aditiya Hermawan Universitas Buddhi Dharma

Abstract

At present, there are still many companies or trading businesses, or other basic food shops that do not use technology such as computers. At the Mukara Store, they have used a computer and implemented the cashier application to calculate buyers' purchases at the Mukara Store. The process of selling activities at the Mukara Shop continues and so does the data generated in the database the longer it will grow. A lot of data is just left. Data Mining is used to dig up information from unused data into useful data for business development in Mukara Stores. Because there are many buyers who come to the Mukara Shop, most buyers often forget to buy items that are usually bought together at one time. Therefore, the authors take transaction data in the Mukara Store, to obtain a purchase pattern in the Mukara Store which is a problem in the Mukara Store, and the results of the purchase pattern are applied to the goods shelf or storefront in the Mukara Store. The author processes transaction data using Data Mining. Some techniques that are often cited in the Data Mining literature include the Association of Rule Mining, Clustering, Classification. One of the Techniques and Methods used by the writer for the problems that exist in the Mukara Store is the Association Algorithm such as the Apriori Algorithm and the FP-Growth Algorithm used to find the purchase patterns in the Mukara Shop, then the results of the purchase pattern are applied in the arrangement of goods racks or storefronts. at the Mukara Shop. Like the products that are bought together are placed on the same shelf, so that buyers do not forget to buy products that are often bought together. The results obtained from transactions at the Mukara Store with a minimum support of 0.2 or 20% and a minimum confidence of 0.1 or 10% are the Egg Noodle Products with Soy Sauce, Soy Sauce products with Vermicelli, Vermicelli products with Soy Sauce, Ketchup products with Egg Noodles. The conclusion is that if a buyer buys egg noodles, then it is likely 81.5% to buy soy sauce. If the buyer buys Ketchup products, then it is likely 82.6% to buy vermicelli. If the buyer buys vermicelli products, then the possibility of 92.7% to buy soy sauce. If the buyer buys soy sauce, then it is likely 47.83% to buy egg noodles.

Published
2020-05-25
How to Cite
CHAILES, Anwan; HERMAWAN, Aditiya. Application of Data Mining Method to Determine Purchasing Patterns Using Apriori Algorithms and Fp-Growth in Mukara Stores. ALGOR, [S.l.], v. 1, n. 2, p. 1-8, may 2020. ISSN 2715-0577. Available at: <https://jurnal.ubd.ac.id/index.php/algor/article/view/333>. Date accessed: 04 july 2020.