Implementasi Data Mining Pada Penjualan Sepatu Menggunakan Algoritma Apriori (Kasus Toko Sepatu 3Stripesid)

Abstract

Stock of goods is an important thing in the world of shops, stock of goods that are not carried out optimally will result in a vacancy of one of the available items. Likewise, too much stock of goods will cause over stock. This also happens at the 3stripeds.id store where there is often a vacancy in one of the inventory items purchased by customers, due to the lack of information regarding inventory control habits. So it is necessary to extract information on transaction data. The Apriori algorithm can help find out the names of items with the most sales. The a priori algorithm is a type of association rule in data mining, an association can be known by two benchmarks, namely support and confidence. Support (support value) is the percentage combination of these items, while confidence (certainty value) is the relationship between items in the association rules. The results obtained from the a priori algorithm process are combinations of items or rules with association values in the form of support values and confidence values. the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 3% and a minimum confidence of 10%, while the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 30% and minimum confidence 85% and there are 2 highest itemsets with 30% support and 100% confidence.Stock of goods is an important thing in the world of shops, stock of goods that are not carried out optimally will result in a vacancy of one of the available items. Likewise, too much stock of goods will cause over stock. This also happens at the 3stripeds.id store where there is often a vacancy in one of the inventory items purchased by customers, due to the lack of information regarding inventory control habits. So it is necessary to extract information on transaction data. The Apriori algorithm can help find out the names of items with the most sales. The a priori algorithm is a type of association rule in data mining, an association can be known by two benchmarks, namely support and confidence. Support (support value) is the percentage combination of these items, while confidence (certainty value) is the relationship between items in the association rules. The results obtained from the a priori algorithm process are combinations of items or rules with association values in the form of support values and confidence values. the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 3% and a minimum confidence of 10%, while the results of the a priori algorithm testing process produce association rules formed from a combination of items that meet a minimum support of 30% and minimum confidence 85% and there are 2 highest itemsets with 30% support and 100% confidence.