Elbow and Silhouette Methods for K Value Analysis of Ticket Sales Grouping on K-Means

Increasingly fierce business competition forces people to develop businesses to compete in selling by looking for leads to increase revenue from business sales. The development of the digital era affects the use of the internet for the process of searching for information and online purchases, such as purchasing tickets and digital products. Film is one of the industries in the entertainment world that brings a new atmosphere for people to watch films that can be watched anywhere and also online and offline. Ticket sales data that is being processed is clustered using K-Means. The clustering process and evaluation to determine the best K value in the clustering process uses the Elbow and Silhouette Score Methods. Grouping is obtained if the grouping results are not significantly different when the grouping process is carried out on the "ticket_sold" and "ticket_price" attributes. Based on the results of the Elbow Method, the result is that the value of k = 3 to the value of k = 5 is the optimal value for carrying out the data grouping process for the case of selling movie tickets. While the results of a good cluster distribution are at a value of k = 5 with a value of 0.60 using the Silhouette Score..