PENENTUAN POLA FREKUENSI PENJUALAN MAKANAN MENGGUNAKAN ALGORITMA APRIORI (Studi Kasus Foodcourt Universitas Muhammadiyah Sukabumi)
Total View This Week0
Total View This Week0
Institusion
Universitas Muhammadiyah Sukabumi
Author
Faujiah, Nadia
Subject
T Technology (General)
Datestamp
2019-12-19 07:12:30
Abstract :
Data mining is defined as mining data or assistance to obtain valuable and useful information in a very large base data, the most important thing in data mining is the rules for determining the frequency pattern of an itemet called the Association Rules (Association Rules). There are several algorithms included in the Fp-grow algorithm association and a priori algorithm, but the authors chose the a priori algorithm for research applications. With the existence of daily sales activities in Foodcourt, ummi will make more and more data, a large amount of data will make storage accumulate more and more if it is not profitable. Therefore the writer will make a transaction data analysis using the rule association method with a priori algorithm. The data that the author uses receive 4957 transactions obtained from transactions in September - October 2018. In this study the item-set used is the date of the transaction and menu. To analyze and make a grouping of sales transaction data in Foodcourt by determining the frequency pattern of item-sets called association rules using a priori algorithm so as to produce a complete pattern of consumers buying food. The resulting pattern can be made in a sales strategy by the food court.
Data mining is defined as mining data or assistance to obtain valuable and useful information in a very large base data, the most important thing in data mining is the rules for determining the frequency pattern of an itemet called the Association Rules (Association Rules). There are several algorithms included in the Fp-grow algorithm association and a priori algorithm, but the authors chose the a priori algorithm for research applications. With the existence of daily sales activities in Foodcourt, ummi will make more and more data, a large amount of data will make storage accumulate more and more if it is not profitable. Therefore the writer will make a transaction data analysis using the rule association method with a priori algorithm. The data that the author uses receive 4957 transactions obtained from transactions in September - October 2018. In this study the item-set used is the date of the transaction and menu. To analyze and make a grouping of sales transaction data in Foodcourt by determining the frequency pattern of item-sets called association rules using a priori algorithm so as to produce a complete pattern of consumers buying food. The resulting pattern can be made in a sales strategy by the food court.
Download
book
BibTex
Latex, Jabref
cloud_download
Original Resource
url resource Institution
