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Kurniasari, Dian and Mahyunis, Ranti Vidia and Warsono, Warsono and Nuryaman, Aang (2023) Implementation of Artificial Neural Network (ANN) using Backpropagation Algoritm by Comparing Four Activation Funtions in Predicting Gold Prices. Kumpulan Jurnal Ilmu Komputer (KLIK), 10 (1). pp. 93-105. ISSN 2406-7857

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Implementation of Artificial Neural Network (ANN) using Backpropagation Algorithm by Comparing Four Activation Funtions in Predicting Gold Prices.pdf
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Abstract

The trend in global currency values is speedy and fluctuating due to the recession caused by the Covid-19 pandemic. That causes investors to flock to buy gold assets. Therefore, it is necessary to predict the price of gold from a business and academic perspective to obtain a reasonable gold price prediction model. This study applies the Backpropagation Algorithm by determining the best ANN model structure based on four activation functions: Sigmoid, Tanh, ReLU, and Linear, as well as learning rate values, namely 0.01 and 0.001. The results are the best ANN model structure with four nodes in the input layer, four nodes in the hidden layer and the output layer using the Linear activation function and a learning rate of 0.01. Based on the structure of the model, the MSE value is 0.00051, the MAPE value is 1.9798%, and the accuracy is 98%.

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User: DIAN KURNIASARI
Date Deposited: 05 May 2025 04:23
Last Modified: 05 May 2025 04:23
URI: http://repository.lppm.unila.ac.id/id/eprint/54505

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