Penerapan Algoritma K-Medoids untuk Klustering Bakery dan Cake yang Laris

Abstract

The culinary business has been a promising business from the past until now, there are many kinds of businesses that are in demand, namely heavy food to light food. One of the promising businesses currently is the bakery and cake shop business. The large amount of sales competition makes the owner enthusiastic about competing to create delicious flavors and have characteristics that are different from other businesses. Mawar Bakery and Cake shop is a culinary business shop made from flour and has many branches in North Sumatra. The many delicious products created can attract the attention of customers, but every month the management must record the products that are sold a lot and the lack of interest. This is done to anticipate losses that occur every month. Therefore, the author created clustering data mining to group bakery and cake sales that were sold a lot and those that were not. The K-Medoids method is able to become an algorithm that is able to carry out the process of collecting data on Mawar bakery and cake sales because the algorithm is practical and efficient in grouping data. The results of data from sales that have been grouped can be concluded that cluster 2 is the best-selling because it gets the highest score