ALGORITMA K-NEAREST NEIGHBOR TERHADAP PELUANG MAHASISWA MENJADI AKTIVIS KAMPUS PADA JURUSAN MATEMATIKA UNIVERSITAS NEGERI PADANG

The purpose of this study was to predict whether mathematics students at Padang State University have the opportunity to become campus activists using the K-Nearest Neighbor Algorithm (KNN). This research will be used as a benchmark to calculate how many mathematics students can become activists at Padang State University. The K-Nearest Neighbor Algorithm (KNN) is a machine learning algorithm that has resistance to training data where there is a lot of noise and is more effective for large data. The K-Nearest Neighbor Algorithm itself is a distance-based data classification process for determining the closeness between different data. become the closest neighbor data and choose a class or category based on the K category of nearest neighbors. In this research, for the first step, data was collected on mathematics students based on several factors that influenced them to become activists, and an analysis of finding distance using the Euclidean distance was carried out. 46% are not activists