Kurniasari, Dian and Mahyunis, Ranti Vidia and Warsono, Warsono and Nuryaman, Aang (2023) IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES. Kumpulan JurnaL Ilmu Komputer (KLIK), 10 (1). pp. 93-105. ISSN 2406-7857
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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%. Keywords: Artificial Neural Network, Backpropagation, Gold Price Prediction, Activation Function, Model Structure.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| 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/54506 |
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IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES. (deposited 04 Apr 2023 00:56)
- IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK (ANN) USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES. (deposited 05 May 2025 04:23) [Currently Displayed]
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