The rapid development of technology has an impact on how data is collected. A high level of data productivity will be in vain if it is not followed by the ability to process data that can produce information that helps the development of the organization. This study aims to help the Promotion Section of UNKRISWINA SUMBA in mapping the characteristics of the target schools and then provide alternative promotion strategies as input in formulating forms of institutional promotion. The data used is in the form of student data who have registered at UNKRISWINA SUMBA since 2016 – 2020. Data processing uses the concept of data mining by applying the K-Means algorithm. K-Means algorithm is used for clustering promotion target schools as many as 4 clusters. Cluster determination is carried out using the elbow method to determine the optimal value of k to perform calculations. Based on the results of processing based on the K-Means algorithm, it is known that as many as 8 schools in cluster 0 are the schools with the most students enrolling in UNKRISWINA SUMBA, 76 schools in cluster 1 are schools with the fewest students enrolling in UNKRISWINA SUMBA, 21 schools those in cluster 2 are schools with quite a lot of students enrolling in UNKRISWINA SUMBA, and 1 school in cluster 3 is a school with quite a number of students enrolling in UNKRISWINA SUMBA but focusing on the Economic Development and Management study program.