Analysis Of Student Satisfaction Feasibility Evaluation In Learning Materials With The K-Nearest Neighbor Model

Abstract

Evaluation of student satisfaction is an important component in the educational process. This research aims to assess the level of student satisfaction with learning material. The analysis used is satisfaction survey analysis in viewing variables, analyzing variables and quantitative data analysis. The importance of a personal approach in education, where effective teaching skills and quality interaction between teachers and students play a vital role in increasing learning satisfaction. The importance of the analysis shows that the KNN model has high accuracy in classifying student satisfaction levels. The research results show that student satisfaction is influenced by the relevance of the material, teaching methods, and interaction with the instructor. The results of this research provide recommendations for increasing student satisfaction including curriculum adjustments, teacher training, and development of more interactive learning materials. It is hoped that this research can provide valuable input for schools and educators in improving the quality of teaching and relationships with students. Analyze the KNN model to find the largest number of classes from the nearest neighbors and set that class as the test data class. In the ranking order there are more satisfied categories. So, data with rk = (5; 8; 8; 9) is included in the Satisfied category. analysis results: V1 is Dissatisfied with a value of 2, V2 with a Satisfied value of 2.828, V3 is Satisfied with a value of 3.464, V4 with a Dissatisfied value of 3.605 and finally V5 with Satisfied worth of 4.123.