<![CDATA[The UNASMAN Language and Character Development Institute (LBPK) is used by all UNASMAN students to improve their English skills so they can continue their studies abroad and as alumni can later compete in the world of work with other alumni both nationally and internationally. This research aims to group student scores in the English Sunset program at the Unasman Language and Character Development Institute (LBPK) using the K-Means method. The K-Means method was chosen because of its effective ability to group data based on similarity of attributes, making it possible to identify groups of students with similar value characteristics. Student score data is collected, processed and analyzed using the K-Means algorithm to determine the optimal number of clusters. The research results show that students can be grouped into three main clusters: students with high scores, medium scores, and low scores. This information provides valuable insight for LBPK in designing more targeted teaching strategies and providing additional support for student groups in need. Feasibility analysis from a technological and operational perspective shows that this system can be implemented with adequate infrastructure and sufficient training support for staff and lecturers. This research confirms that the K-Means method can be used effectively to improve the qualitys of learning at LBPK Unasman.]]>
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