@thesis{thesis,
		author={Ibrahim  Muhammad Yusuf and Kusuma  Dine Tiara and Putra  Rakhmadi Iriansyah},
		title ={Implementasi Algoritma Frequent Pattern Growth untuk Menentukan Rekomendasi Menu Makanan dan Minuman Pada Rumah Makan Dini},
		year={2020},
		url={http://156.67.221.169/4228/},
		abstract={Using transaction data that occurs to determine food and beverage recommendations is a difficult challenge for early restaurant owners to know how many transactions have occurred so that these transactions are only kept as records because they cannot be utilized. The method that can be done is to look for the transaction data association rules that occur using the FP-Growth algorithm and to test the level of compliance of the association rules that have been made using the Lift Ratio. The results of the association rule order and the best accuracy obtained from 140 data as testing data with a minimum support threshold value of 15% and a minimum confidence threshold value of 55% produce an association rule, namely "if you order chicken noodles you will order meatballs", where the rule The association produced a support value of 18.57%, a confidence value of 59.09% and an Lift Ratio value as the value of the formation of a rule of 1.45 which indicates that the rule is valid or categorized as positively correlated because the value of the Lift Ratio > 1.}
		}