Suitability of the internship based on the interests and interests of students very influential on the smoothness and success when working practice field. At SMKN 1 Ponorogo the selection of internships is still in place Manually by the school. This approach is still considered less effective in channeling students’ interest in the criteria possessed by
partner company. This research is carried out so that the design of a system recommendation by combining the K-Means Clutering and KNN runs effectively and appropriately towards the interests of students and the character of the company
partner. This research approach is experimental, which begins with collection of interests from students by utilizing the K-Means algorithm Clustering for student data collection, data preprocessing and grouping company, while the KNN algorithm to recommend the place Apprenticeship based on the compatibility between the interests of students with the character owned by a partner company. This system is built using Python as the basis of programming and Flask as a supporting framework, testing is done to determine the ability of the system to produce Apprenticeships that match the interests of students and character partner company. The results of the test were conducted that the combination K-Means Clustering and KNN algorithms are suitable and precise in performing Recommendations for the internship. By doing this system, the process of placing The place of internship can be done more systematically than manual approach used before.
Keywords: Recommendation System, K-Means Clustering, K-Neerest Neighbor, Place of Internship, SMK