The Formula Study in Determining the Best Number of Neurons in Neural
Network Backpropagation Architecture with Three Hidden Layers
Dublin Core
Title
The Formula Study in Determining the Best Number of Neurons in Neural
Network Backpropagation Architecture with Three Hidden Layers
Network Backpropagation Architecture with Three Hidden Layers
Subject
Neural Network, Backpropagation, 3-Layer Hidden, Number of Neurons
Description
The researchers conducted data simulation experiments, but they did so unstructured in determining the number of neurons in
the hidden layer in the Artificial Neural Network Back-Propagation architecture. The researchers also used a general
architecture consisting of one hidden layer. Researchers are still producing minimal research that discusses how to determine
the number of neurons when using hidden layers. This article examines the results of experiments by conducting training and
testing data using seven recommended formulas including the Hecht-Nelson, Marchandani-Cao, Lawrence & Fredrickson,
Berry-Linoff, Boger-Guterman, JingTao-Chew, and Lawrence & Fredrickson modifications. We use rainfall data and
temperature data with a 10-day type for the last 10 years (2012-2021) sourced from Lombok International Airport Station,
Indonesia. The training and testing data used showed the results that in determining the number of neurons on the hidden-1
screen, it was more appropriate to use the Hecht-Nelson formula and the Lawrence & Fredricson formula which is more
suitable for use in the 2nd & 3rd hidden layer. The resulting research was able to provide an accuracy rate of up to 97.79%
(temperature data) and 99.94% (rainfall data) with an architecture of 36-73-37-19-1
the hidden layer in the Artificial Neural Network Back-Propagation architecture. The researchers also used a general
architecture consisting of one hidden layer. Researchers are still producing minimal research that discusses how to determine
the number of neurons when using hidden layers. This article examines the results of experiments by conducting training and
testing data using seven recommended formulas including the Hecht-Nelson, Marchandani-Cao, Lawrence & Fredrickson,
Berry-Linoff, Boger-Guterman, JingTao-Chew, and Lawrence & Fredrickson modifications. We use rainfall data and
temperature data with a 10-day type for the last 10 years (2012-2021) sourced from Lombok International Airport Station,
Indonesia. The training and testing data used showed the results that in determining the number of neurons on the hidden-1
screen, it was more appropriate to use the Hecht-Nelson formula and the Lawrence & Fredricson formula which is more
suitable for use in the 2nd & 3rd hidden layer. The resulting research was able to provide an accuracy rate of up to 97.79%
(temperature data) and 99.94% (rainfall data) with an architecture of 36-73-37-19-1
Creator
Syaharuddin1
, Fatmawati2
, Herry Suprajitno3
, Fatmawati2
, Herry Suprajitno3
Publisher
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia
Date
30-06-2022
Contributor
Syaharuddin1
, Fatmawati2
, Herry Suprajitno3
, Fatmawati2
, Herry Suprajitno3
Format
PDF
Language
Indonesa
Type
Text
Files
Collection
Citation
Syaharuddin1
, Fatmawati2
, Herry Suprajitno3, “The Formula Study in Determining the Best Number of Neurons in Neural
Network Backpropagation Architecture with Three Hidden Layers,” Repository Horizon University Indonesia, accessed June 25, 2025, https://repository.horizon.ac.id/items/show/9180.
Network Backpropagation Architecture with Three Hidden Layers,” Repository Horizon University Indonesia, accessed June 25, 2025, https://repository.horizon.ac.id/items/show/9180.