<![CDATA[The problem in this study is that there has never been a prediction of student graduation, especially the Faculty of Engineering using the KNN algorithm at Bina Insan Lubuklinggau University and it is necessary to evaluate student graduation both in a timely and untimely manner so that we can compare from year to year whether it is increasing or decreasing. student graduation each year. The research method used is the method of analysis by making predictions using the KNN algorithm. Data collection techniques used are observation, interviews, documentation, and literature study. The results of this study are the implementation of predictions for graduating students at Bina Insan University using the KNN algorithm in Rstudio. Implementation is done by preparing a dataset that will be used for predictions. The dataset is divided into two, namely training data and testing data. Then set the category column as the predictive determinant. Furthermore, the data that has been prepared is normalized so that it can be implemented into the KNN algorithm. Installing the required libraries into Rstudio. Perform prediction calculations using KNN. Displays accuracy results and displays graphs. Based on the results and discussion that has been done, it is obtained that the prediction of Computer Systems Engineering students graduating on time is 0.91 or 91%. Whereas for students majoring in Information Systems graduating on time is 0.70 or 70%. Then for the Informatics Engineering major, graduates on time are 0.80 or 80%. Based on the results of these predictions, the student with the highest prediction of graduating on time is Computer Systems Engineering with a passing percentage of 91%.]]>
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