Data Mining Systems to Determine Sales Trends and Quantity Forecast Using Association Rule and CRISP-DM Method

Customer is the most important part of the business, the data taken is a record of the purchase transactions of products purchased in each transaction. With the existence of data mining expected hidden and unknown pattern can be utilized in customer purchasing pattern. Then apriori algorithm as the basis of which there are methods of association rules and CRISP-DM in this system can determine thats the most products of interest by customers by applying data mining system on each transaction data. Result of data mining processing to determine sales trend towards a sales product where with this sales trend management team can analyze by disclosing which product sales follow steep growth path and which stall or decrease. An example of data mining to determine the sales trend pattern based on a combination of 2 products. Where it has been determined is Threshold Support = 0.1 and Threshold Support x Confidence = 0.05 and for the quantity forecast of 23 products into the sample and who managed to enter into quantity forecast only 13 products. Where the successful product is determined quantity forecast only that has a support value above the threshold support value that has been determined by the authors in this paper. Result of the quantity forecast of the input specified such as Threshold Support: 0.2, Threshold SupportxConfidence: 0.1 and Percent Forcast: 0.15.