<![CDATA[Customer segmentation is one of the important approaches in the banking industry to improve the effectiveness of marketing strategies and risk management. This study aims to compare the performance of two commonly used clustering algorithms, namely K-Means and DBSCAN, in segmenting customers based on bank marketing data. The data used comes from the bank marketing dataset available on Kaggle, including attributes such as age, type of employment, marital status, education level, savings balance, and previous campaign history. The analysis process includes data pre-processing, feature extraction, data standardization, and implementation of the clustering algorithm. Evaluation of the results using the Silhouette Score metric shows that the K-Means algorithm produces a higher Silhouette Score value and a lower Davies-Bouldin Index (DBI) than DBSCAN indicating that K-Means is able to produce more solid (compact) clusters and has clearer boundaries between data groups. However, DBSCAN shows advantages in the ability to detect noise and group data with irregular shapes. Thus, the selection of the best algorithm is highly dependent on the characteristics of the data and the purpose of the segmentation. For bank marketing data with a relatively uniform distribution, K-Means is a more optimal choice.]]>
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