Abstract
Student Satisfaction Index has a very important role in the university environment as it is closely related to the accreditation assessment process. In this context, Nurul Jadid University faces shortcomings in calculating student satisfaction, which has the potential to disrupt the efficiency of time use and have an overall negative impact. As a solution, this research aims to cluster student answers using the K-Means algorithm implemented through the Streamlit web platform with the Python programming language. The results showed that this approach was able to produce excellent clustering, with an accuracy rate of 97%. The main objective of this research is to improve efficiency in the process of measuring student satisfaction levels, with the hope of making a significant contribution to improving the quality of university services and the process of evaluating university accreditation more efficiently. As such, this research has important implications in improving the overall performance of the university in the face of challenges.
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