PREDIKSI JUMLAH PENDUDUK DENGAN ALGORITMA RADIAL BASIS FUNCTION (STUDI KASUS: DESA KALIERANG)

  • D.D Ihsana Latif Universitas Peradaban
Keywords: predictions, population, radial basis function, K-means

Abstract

For identifying the rate of population growth in an area ata future time, a prediction is needed with Radial Basis Function Neural Network algorithm for prediction population number. In this study the decided center using an algorithm K-Means clustering. The study used data on population number,
mortality rates, and the brithing rate. The data used is monthly data starts from January 2015 to December 2018 with 144 data as training data and data from January 2019 to August 2020 with 57 data as testing data. MSE results from RBF network training of 0,00033023 and MSE testing of 0,99301. And the MAPE value on this assessement is 14,6968% with accuarcy at 85,3032%

Published
2021-12-30