<![CDATA[The advancement of information technology has encouraged people to shop more confidently, including for men's skincare products. Although data indicate that men's interest in skincare remains relatively low, sales of Kahf Face Wash show high figures. In this context, consumer reviews on e-commerce platforms serve as a valuable source of information for understanding customer satisfaction and experience. This study aims to group consumer comments on Kahf Face Wash products from the Kahf Official Shop using the K-Means clustering method. A total of 4,966 consumer comments were collected automatically through web crawling techniques. These comments then underwent several text processing stages, including case folding, cleaning, tokenization, normalization, removal of stop words, and stemming. After the cleaning process, 2,431 comments remained for analysis. The textual data was transformed into numerical representations using the TF-IDF method, and the optimal number of clusters was determined using the Elbow method, which indicated the optimal value at k = 3. The clustering results categorized the comments into three groups: purchase experience (1,506 comments), product effectiveness (474 comments), and delivery and service (451 comments). Visualization was conducted using PCA and bar charts to better illustrate the distribution and proportion of comments in each cluster. Evaluation of the clustering results using inertia and the Davies–Bouldin Index revealed that the model effectively grouped the comments with a reasonably high quality. This study makes a significant contribution by helping companies analyze customer behavior through an unsupervised learning approach. This method enables companies to efficiently extract structured insights from unstructured reviews, which can be utilized to enhance service quality, marketing strategies, and future product development.]]>
