<![CDATA[This study aims to implement the K-Means algorithm to improve the kebaya clustering model to support the rental marketing strategy at Gifaattire Store. The K-Means algorithm was used to analyze eight months of historical kebaya rental data, focusing on the attributes of kebaya type and color. Using the Knowledge Discovery in Database (KDD) approach, the research conducted data selection, preprocessing, transformation, data mining, and evaluation of clustering results. Davies-Bouldin Index (DBI) was utilized to assess the quality of clustering, resulting in an optimal value of 6 clusters with a DBI of 0.580. The results showed that each cluster has unique characteristics that reflect customer demand patterns. Cluster 0, the largest cluster, indicates kebayas with high demand but limited color variations. In contrast, Cluster 1 indicates kebayas with a wide variety of colors but specific demand. This information enables Gifaattire Store to design more targeted data-driven marketing strategies and improve stock management efficiency. The research contributes to the development of literature on the application of K-Means in the fashion rental sector and offers practical insights into understanding customer preferences.]]>
