Analysis of Student Selection Models Using K-Means Clustering and K-Nearest Neighbor Classification Algorithms - Imam Fakhri Muhammad; Syafri Arlis; Musli Yanto (Universitas Putra Indonesia YPTK Padang, 2026)
The high level of student interest in the selection process poses challenges, including student admission management. The selection process generally consists of several stages, ranging from administrative tests, academic tests, psychological tests, and physical fitness tests. Based on this, the purpose of this study is to develop an approach that can help evaluate student readiness objectively and based on data. This study aims to analyze student selection by applying the concept of data mining using the K-Means and K-Nearest Neighbor (KNN) algorithms. The K-Means algorithm is used to group student data into several clusters based on the similarity of characteristics. Meanwhile, the K-Nearest Neighbor algorithm works by classifying new data based on similarity or the closest distance. The research dataset consists of 124 student data points obtained from the Arka Padang tutoring center headquarters. Based on this study, the results show that the application of the K-Means and K-Nearest Neighbor (KNN) algorithms demonstrates that both methods are capable of processing student data to identify patterns and levels of readiness for selection, achieving an accuracy of 92.10%. Thus, this method is considered reliable in supporting the process of evaluating student readiness. This research contributes to the understanding of the application of data mining concepts to evaluate and analyze student readiness levels and demonstrates how the K-Means and KNN algorithms can be used in the process of classifying students objectively and based on data.