Implementation of Forgy Initialization and K-Means++ Algorithms in the K-Means Clustering Method for Sales Data Analysis of Dazzle Store - Abdillah, Muhamad Hilmi (Telematika, 2025)
Objective: To determine the results of K-Means Clustering calculations by applying K-Means++ and Forgy initialization methods in analyzing sales data at Dazzle accessory store, as well as to identify the optimal number of clusters using the silhouette coefficient.Method: This study implements the Forgy initialization and K-Means++ algorithms in the K-Means Clustering method, along with an evaluation of the optimal number of clusters using the silhouette coefficient method.Results: The application of Forgy initialization and K-Means++ successfully improved clustering outcomes more optimally compared to the pure initialization method. The highest silhouette coefficient evaluation score was 0.9232095222373023 for K-Means++ and 0.8822890619277 for Forgy initialization. This result is clearly better than the pure initialization method, which only achieved a score of 0.8816344025002508.State of the Art: This study builds upon previous research. The innovation lies in the implementation of a combination of K-Means Clustering with Forgy initialization and K-Means++ initialization methods.