IMPLEMENTASI ALGORITMA APRIORI DAN FORECASTING PADA TRANSAKSI PENJUALAN - Irvan Firnando; Dixsen Dixsen; Tony Tony; Vincent Wijaya; Surianto Surianto; Eri Yanto; Deny Jollyta (Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan)
One of the most important parts of a retail business or product distribution company is inventory management. Transactions with very large amounts in a certain period make the transaction data on sales, prices, and availability of goods must be managed properly. This study was delivered to facilitate the company in determining policies related to sales and availability of goods through the purchase pattern of association rules and sales predictions using the Moving Average method. Association rule is data mining techniques contained in the Apriori algorithm. This algorithm is able to shows random relationships in a number of transactions. The test resulted in three patterns of purchasing goods with the highest frequency namely Milo Activ-go UHT Cmbk 36x115ml, Bear Brand RTD Milk 30x189ml and Milo Activ-Go UHT Cmbk 36x190ml with values of 46.17%, 41.97% and 15.39%. The Moving Average result, sales predictions produce a total of 3669, 3280, and 2619 for each item that can be prepared in the next period. This can be a company's reference in predicting goods that are in demand or not, determine the number of sales and prioritize the procurement of goods based on the rules of the association produced.