PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE)

  • Nita Suryani Department of Mathematics, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia
  • Arnita Arnita Department of Mathematics, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia
  • Rinjani Cyra Nabila Department of Mathematics, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia
  • Amanda Fitria Department of Mathematics, Faculty of Mathematics and Natural Sciences, Medan State University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0467-0474
Keywords: Prediction, Poverty rate, K-Means, GRNN

Abstract

Poverty reduction is a crucial issue and the primary The North Sumatra Provincial government's main concern is lowering the poverty rate, which is a crucial issue. The Province of North Sumatra in Indonesia, one of many nations affected by the Covid-19 pandemic, is particularly troubled economically. In this study, poverty levels were mapped using the K-Means algorithm, and GRNN was then utilized for modeling and prediction. The data source used is time series data from 2010 to 2020 from the Central Statistics Agency (BPS), which includes variables X covering population, health, education, unemployment, and asset ownership and variable Y representing poverty level. The goal of this study is to choose the best model for estimating poverty levels in North Sumatra Province. The districts and cities of Deli Serdang and Medan have the greatest rates of poverty, according to the K-means algorithm's mapping of poverty levels. Additionally, the results of the predicting produced MSE values of 0.004659 and RMSE values of 0.00002108. The value of the smoothness parameter is 0.01.

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Published
2023-04-20
How to Cite
[1]
N. Suryani, A. Arnita, R. Nabila, and A. Fitria, “PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE)”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0467-0474, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Nita Suryani, Arnita Arnita, Rinjani Cyra Nabila, Amanda Fitria

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