Implementation of K-NN Algorithm to classify the Scholarship Recipients of Aceh Carong at Universitas Malikussaleh

Abstract

In an effort to increase the efficiency of the scholarship selection process, this research aims to implement the K-Nearest Neighbors (K-NN) algorithm in the classification of scholarship recipients. The research method involves collecting data on scholarship receipts from several previous years based on predetermined criteria such as father's job, mother's job, parent's income, number of parents working, father's last education, and mother's last education. Next, the K-NN algorithm is applied to classify prospective scholarship recipients based on the similarity of their profiles to students who have received previous scholarships. The results of this research indicate that the implementation of the K-NN algorithm in the classification of scholarship admissions at Malikussaleh Aceh Carong University can increase selection accuracy. The experimental results of the accuracy values obtained show that using the K-Nearest Neighbors algorithm with the Euclidean Distance approach and a value of K = 3 produces an algorithm accuracy level of 87.55%. Thus, the K-NN algorithm can be a useful method for scholarship selectors to support more precise and objective decision making.