<![CDATA[Facebook Live is a social media platform owned by Facebook that allows users to broadcast videos directly or live stream via the internet. Users can share moments in real-time with friends, followers, or members of certain groups. The platform allows anyone with a Facebook account to create live video broadcasts from a mobile device or computer equipped with a webcam. Many Micro, Small and Medium Enterprises (MSMEs) use Facebook Live as a tool to sell products or services directly to their audience. This strategy is increasingly popular in direct marketing on social media, especially in countries such as China and Thailand. Sellers on Facebook Live, known as Facebook Live Sellers, broadcast live on the platform to introduce products or services. They explain all the features offered, answer questions from viewers, and encourage them to make a purchase immediately. To increase buyer interest, they often offer special offers or discounts. Facebook Live Sellers can also be considered a form of influencer marketing, where individuals or businesses build a loyal following and use their influence to promote products and services. Despite the potential benefits, Facebook Live Sellers also face challenges. They interact directly with potential buyers, who may sometimes be dissatisfied with the product offered or the way the seller promotes it. Therefore, evaluations such as comments, reactions (such as like, unlike, angry), and other interactions during broadcasts are important. This research aims to group potential buyers' reactions during Facebook Live broadcasts as a strategy to overcome several problems in direct sales via this platform. In addition, grouping by the number of likes and comments can help sellers identify the most active groups of buyers and have the potential to become loyal customers. The number of data samples was determined using the Solvin method so that the dataset that became the data sample was 341 data. The methods used for grouping are K-Means and AHC (Average linkage) with the final results showing that the amount of data grouped into three clusters by both methods is the same, with most of the data being in Cluster 0, namely 98.5% of the total data sample. . Cluster 1 has a small amount of data, namely 0.6%, while Cluster 2 has 0.9% of the data sample.]]>
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