Clustering Potato Seeds Using DBSCAN Algorithm in Optimizing Sales - N P Dharshinni; Andre Putra Persada Tarigan (Sean Institute, 2021)
Potato seed shop CV. ASAP sells various types of potato seeds which are quite trusted by potato farmers. The payment methods used so far are cash and credit payments, but the shop owner never has a balanced stock so that customer demand is always not in accordance with the stock of seeds, resulting in the shop is at a loss. Based on the problems experienced by potato farmers, it is necessary to group potato seed sales data using the DBSCAN clustering algorithm. The DBSCAN algorithm groups sales data into several cluster groups based on epsilon values and minimal points, besides that this algorithm can detect noise in data grouping so that the grouping results obtained are better than other algorithms. The purpose of this study was to apply the DBSCAN algorithm to potato seedling sales data to obtain results in knowing the types of potato seeds that were most purchased by customers from potato seed sales with cash payment methods and credit payments. The results of the application of the DBSCAN algorithm found that the most purchased by customers using the cash payment method were the type of spread seeds, class D potato seeds, class A potato seeds, and class C potato seeds with the test value of epsilon 1,2 and Minimum points 1 while the sale of seeds Potatoes, the most purchased by a customer using credit payment method are the type of potato produced by class C and potato spread seeds with the test value of Epsilon 2 and Minimum points 1.