DATA MINING ANALISA POLA PEMBELIAN KONSUMEN PADA TRANSAKSI PENJUALAN ACCESSORIES HANDPHONE MENGGUNAKAN ALGORITMA APRIORI - Muhammad Milzam (2021)
Data mining is a process of analyzing a set of data that can produce new information. This new information can be used as a decision support tool. But it is different when the amount of data that is owned is large or large, problems in processing will arise. Data mining can help process large or large amounts of data. One example that the author takes is transaction data at a shop in the East Jakarta area. Transaction data at the store are still not good at processing sales data. By using a priori algorithm and with the association rule method to process sales transaction data at the store. The seller knows which items are most often purchased by consumers. The first stage in calculating the a priori algorithm to find the association rule is to write down product purchase data (goods query). Next, create an itemset table to calculate the number of purchases for each item. Then make a combination of 2 itemset in each query and the frequency of each is calculated according to the data in the table. So as to produce a minimum value of support and a minimum value of confidence. After the formation of several itemset that meet the minimum value of support and the minimum value of confidence, then a frequent itemset will be formed which results in several association rules. From the results of the analysis, the expected results in this study can provide information on consumer purchasing patterns to assist in providing product stock.