Predicting Students' Mathematical Decision Making Using k-Nearest Neighbor Technique - Giyanti Giyanti; Rina Oktaviyanthi (Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam, 2024)
Abstract: Mathematical decision-making abilities are mathematical information processing through risk evaluation and investigation of various possibilities and perspectives. However, the evaluation of mathematical decision-making abilities is still limited to high, medium, and a low level based on test scores and is not predictive naturally. The purpose of this study is to identify the parameter k in the k-nearest neighbor technique, which serves as the nearest-neighbor value determining the mathematical decision-making abilities of students to be predicted. The process of data exploration to prediction is employed by the data mining approach with the k-Nearest Neighbor method. A total of 65 first-year students taking Calculus I included as research samples. The research results show that a parameter value of k=15 is better at predicting the closeness of the mathematical decision-making with an accuracy of 93.33%, associated with the excellent category. The parameter value representing the closeness of the decision-making abilities level among students serves as a reference for teacher predictions to categorize students and create diversified teaching materials.