Application of the K-Nearest Neighbor (KNN) Algorithm in Machine Learning to Predict the Selection of Undergraduate Study Programs Based on New KIP Lecture Students

Higher education plays a vital role in shaping the future of individuals and society. Choosing the right study program is an important decision for every student, because it will affect their career path and personal development. The KIP Lecture program is present as a government initiative to provide higher education opportunities to students from underprivileged families. However, with the many options of study programs available, new students often have difficulty in determining the study program that suits their interests and abilities. On the other hand, the data of new students that is quite complete and varied opens up opportunities to use machine learning technology in helping the study program selection process. The K-Nearest Neighbor (KNN) algorithm as one of the simple and easy-to-implement machine learning algorithms has the potential to provide more accurate recommendations for the selection of study programs based on student data at STMIK Kaputama. Therefore, this study focuses on analyzing the use of the KNN algorithm in machine learning to predict the selection of undergraduate study programs. This research aims to identify existing problems, evaluate the effectiveness of KNN in this context, and provide solutions that can be implemented to improve the study program selection process for new students who receive KIP Lecture. It is hoped that it can provide recommendations for the selection of study programs that are more accurate and relevant for new students who receive KIP Lecture at STMIK Kaputama. In addition, this solution can also increase the effectiveness of academic guidance and assist students in achieving better academic and career success.