Machine Learning to Predict Student Satisfaction Level Using KNN Method and Naive Bayes Method

This research aims to apply machine learning techniques in classifying student satisfaction levels at the Faculty of Science and Technology, using the K-Nearest Neighbors (KNN) and Naive Bayes methods. This method was chosen because of its ability to manage classification data and provide accurate predictions regarding student satisfaction with the faculty. It is hoped that this research will provide a deeper understanding of the factors that influence student satisfaction as well as the potential for developing a better evaluation system in the future. This research was carried out through structured stages, starting from selecting the right data to collect relevant information, designing the model by applying the KNN and Naive Bayes methods, to evaluating the performance of the model being built. The data used consisted of 110 student data, where the classification results showed that 104 students were satisfied and 6 other students were dissatisfied with the faculty. The evaluation process produced excellent accuracy, with the Test and Score results and confusion matrix showing an accuracy level exceeding 90%. In conclusion, this research succeeded in showing that the KNN and Naive Bayes methods were effective in classifying the level of student satisfaction at the Faculty of Science and Technology. The results obtained confirm that both methods are reliable in managing and analyzing student satisfaction data efficiently, and provide valuable insights for educational institutions to improve student services and experiences in the future