Analisis Data Penjualan Menggunakan Algoritma Apriori pada Analisis Kopi - Hidayat, Tomi; Munthe, Ibnu Rasyid; Juledi, Angga Putra (Fakultas Sains & Teknologi, Universitas Labuhanbatu, 2024)
Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.