APPLICATION OF THE K-MEANS METHOD FOR CLUSTERING BEST SELLING PRODUCTS IN ICE CREAM SALES

Abstract

From youngsters to adults, ice cream is a culinary item that is highly sought after by a variety of demographics. The ice cream combination, which includes a variety of culinary items including dairy products, sugars, stabilizers, flavor enhancers, and eggs, is frozen to create this dish. Aice Ice Cream has a wide variety of goods, which makes it popular with customers. According to various data that the author has observed from Agent Aice Harun, including the fact that there are still challenges in the stock of goods available to meet consumer demand, it is necessary to forecast sales of Ice Cream Aice products that are most in demand by consumers in order to facilitate stock provision. Due to these problems, the researchers conducted. The K-Means Clustering Algorithm approach will be applied manually during the clustering process, and Python data mining tools will be used for the implementation. The final calculation results are the same, showing that 48 things are the most desirable, 6 items are pretty attractive, and 6 items are less desirable, according to the results of determining centroid values arbitrarily using the Euclidian Distance formula manually using Python Tools. Agent Aice Harun is able to supply goods that are in line with consumer preferences as a result. Python computations and manual calculations get the same conclusion. Therefore, it is evident from these data that the accuracy value attained is 100%.