PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS - ., Noviana; Fauzi, Chairani; ., Sriyanto; Fartesa, Justi (LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia, 2023)
One school attempted to predict the acceptance of new students based on data from the previous year, but the results were inaccurate. The fluctuation in the number of new student admissions is a problem for SMK Negeri 2 Kotabumi in preparing class facilities, uniforms, books to support learning activities and determining steps and policies related to school promotion and targets for new student admissions in the following years. Predicting new student enrollment using the polynomial regression method, and K-Medoids, in processing student enrollment prediction data. The results obtained are Y values that are in accordance with the implementation results using python. For example, in 2018 the value Y = 0.0034x6 - 0.6194x5 + 46.754x4 - 1864.6x3 + 41412x2 - 485358x + 2E+06 = 1744.01 with R = 0.8779 accompanied by the same for each year, whereas for the K- Medoids method obtained in 2018 clustering 0 obtained 73 prospective students in the non-passing category and 19 in the pass category, while for 2019 to 2022 the number of cluster 0 is worth 0 and cluster 1 is worth 92 which means that all participants have passed