<![CDATA[This research focuses on the comparison between two popular algorithms in data science, namely K-means and Agglomerative Clustering algorithms. The main context of this research is customer data segmentation, a very important process in the business world to understand and serve customers better. The main objective of this research is to evaluate and compare the performance of the two algorithms in generating effective and efficient customer segments. In this research, the dataset used is a retail customer dataset. This dataset includes various attributes that reflect customer characteristics and behavior. To measure the performance of both algorithms, this research uses the RFM (Recency, Frequency, Monetary) weighting method. This method is a commonly used method in customer analysis to identify the most valuable customers based on how recently they transacted (Recency), how often they transact (Frequency), and how much their transactions are worth (Monetary). In addition, this research also uses an evaluation metric known as silhouette score. This metric is used to measure how well an object fits into its own cluster compared to other clusters. The results of this study provide valuable insights into the quality of both algorithms in segmenting customer data. It was found that the K-Means algorithm produced a silhouette score value of 0.5087, while Agglomerative Clustering produced a higher value of 0.6363. This suggests that, in the context of this dataset, Agglomerative Clustering may be more effective compared to K-Means. However, further research is certainly needed to validate these findings and to further explore how these two algorithms can be optimized for customer data segmentation]]>
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