Rekomendasi Barang Di Toko Elektrik Menggunakan Algoritma Apriori - Arvian Furqon Yudanar; Sri Hariyati Fitriasih; Muhammad Hasbi (STMIK Sinar Nusantara, 2020)
Each company or organization which wants to survive needs to determine the right business strategies. Sales data for products made by the company will get a lot of data. So it is very unfortunate if there is not repetition analyzing. Its offered variety products with a wide range of products, and sometimes the brand influence people to buy the product, to know the highest sales products, it needs to know the relationship between one product to others, one of them is existing algorithms in mining data algorithms. They are algorithms apriori to be informed, and it can help of this program, products which appear simultaneously knowable. The purpose of the research is to determine the recommendation of goods so that purchases of goods stock are efficient. Apriori algorithms including the type of association rules in mining data. The one-step analysis association phase which is gotten the attention of many researchers to produce efficient algorithms is the analysis of patterns of high frequency (frequent pattern mining). Important or not an association can be identified by the two benchmarks, namely: support and confidence. Support (support value) is the percentage of the combination of these items in the database, while confidence (value certainty) is a strong relationship between the items in the rules of the association. Apriori algorithm can be helpful for the development of marketing strategies. From the validity testing result, the data is efficient if the minimum support more than 10% and the minimum confidence of more than 50%. The calculation needs two different minimum support and minimum confidence to know the best result. The problem is how to increase sales, and find out the interest of buyers in the product. And the results are obtained to decide the layout of the products in the shop window as an effort to increase sales in the store.Keywords: Mining Data, Good Recommendations, Apriori, Algorithm