Analysis Of Increasing Student Service Satisfaction Using K-Means Clustering Algorithm and Gaussian Mixture Models (GMM)

This research analyzes the comparison between two cluster analysis algorithms, namely K-Means Clustering and Gaussian Mixture Model (GMM), to gain a deeper understanding of data structure and model suitability. The results of the analysis show that the silhouette score value from using the K-Means algorithm is 0.44528, indicating relatively good cluster grouping, while the use of the Gaussian Mixture Model produces a silhouette score value of -0.500119, indicating a mismatch between the data points in the cluster and the probability overlap between clusters. Therefore, the conclusion states that based on the silhouette score value, using the K-Means Clustering algorithm is better because it produces better and more cohesive cluster grouping. The results of this analysis are that campuses can use this information to understand student needs more effectively and take appropriate corrective steps.