Classification of Eligibility for Prospective Recipients of the KIP Kuliah Scholarship Using the K-Nearest Neighbor Method at Dehasen University in Bengkulu

Abstract

The Kartu Indonesia Pintar (KIP) Kuliah program aims to help high-achieving students from low-income backgrounds continue their education at the university level. At Dehasen University in Bengkulu, the high number of applicants far exceeds the available quota, while the selection process is still conducted manually by reviewing applications one by one. This situation results in a slow and inefficient selection process that is prone to human error and potentially introduces subjectivity in determining the eligibility of scholarship candidates.This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify the eligibility of KIP Kuliah scholarship candidates based on socioeconomic and academic data. The research stages included importing a dataset of 665 records, data cleaning, data selection, data transformation, Min-Max normalization, and splitting the data into training and testing sets, followed by classification by testing values of K = 3, 5, 7, and 9 using Euclidean distance calculations.  The research results show that the KNN algorithm was successfully applied in the classification process, with the best accuracy achieved at K=7: 63.91%, Precision 53.90%, Recall 63.91%, and F1-Score 57.61%. Thus, the K-Nearest Neighbor (KNN)-based classification system developed can facilitate the initial selection process for prospective recipients of the KIP Kuliah Scholarship more quickly, consistently, and objectively compared to a manual selection process.