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All Journal Jurnal Manajemen Informatika Jayakarta
Napitupulu, Pretty Natalia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Materials Science & Nanotechnology

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PENERAPAN ALGORITMA BACKPROPOGATION DALAM PREDIKSI JUMLAH PENDUDUK DI PROVINSI SUMATERA UTARA Napitupulu, Pretty Natalia; Safii, M.
Jurnal Manajamen Informatika Jayakarta Vol 4 No 1 (2024): JMI Jayakarta (February 2024) Seleksi Paper SENATIKA-4 (October 2023)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v4i1.1300

Abstract

Total Population is the total number of individuals living in an area or country at any given time.country at any given time. However, a large population has several negative effects of social instability, poverty, reduced quality of life, and increased unemployment.quality of life, and increased unemployment. This research discusses the application of the Backpropagation algorithm in predicting population in the province of North Sumatra province. The purpose of this research is to make predictions that can help local governments in development planning and more effective management of resources more effectively. The Backpropagation method is used in training of the artificial neural network model using historical data on the population population and the factors that influence the growth. The results of experimental results show that the resulting model is able to provide fairly accurate predictions with an acceptable error rate. This paper presents the results of research that aims to apply the Backpropagation algorithm in an effort to predict population in North Sumatra province from 2013-2022 using Microsoft Excel and Matlab version 2011b for data processing and analysis. version 2011b for data processing and analysis. The architecture uses three models, namely: 4-5-1, 4-10-1, 4-15-1. The most accurate architecture model is 3-15-1 model which has a Mean Squared Error (MSE) of 0.00000034 and 100% accuracy rate with time 00:05 at epoch 92.
Co-Authors Safii, M.