TELKOMNIKA Telecommunication, Computing, Electronics and Control
PSO optimization on backpropagation for fish catch production prediction
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
PSO optimization on backpropagation for fish catch production prediction
PSO optimization on backpropagation for fish catch production prediction
Subject
Backpropagation, Climate change, Fish production prediction, Particle swarm optimization, RMSE
Description
Global climate change is an issue that is enough to grab the attention
of the world community. This is mainly because of the impact it has on human life. The impact that is felt also occurs in waters on the South Kalimantan region. This is of course can disrupt the productivity of fish in the marine waters of South Kalimantan. This study aims to make fish catch production prediction models based on climate change in the South Kalimantan Province because the amount of productivity of marine fish has fluctuated. This study uses climate data as input and fish production as output which is divided into two, namely training and testing data. Then the prediction is conducted using Backpropagation method combined with Particle Swarm Optimization method. The results of the study produced a prediction model with RMSE of 0.0909 with a combination of parameters used, namely, C1: 2, C2: 2, w: 0.7,
learning rate: 0.5, Momentum: 0.1, Particles: 5, and epoch: 500. While
the model used when predicting testing data produces RMSE of 0.1448.
of the world community. This is mainly because of the impact it has on human life. The impact that is felt also occurs in waters on the South Kalimantan region. This is of course can disrupt the productivity of fish in the marine waters of South Kalimantan. This study aims to make fish catch production prediction models based on climate change in the South Kalimantan Province because the amount of productivity of marine fish has fluctuated. This study uses climate data as input and fish production as output which is divided into two, namely training and testing data. Then the prediction is conducted using Backpropagation method combined with Particle Swarm Optimization method. The results of the study produced a prediction model with RMSE of 0.0909 with a combination of parameters used, namely, C1: 2, C2: 2, w: 0.7,
learning rate: 0.5, Momentum: 0.1, Particles: 5, and epoch: 500. While
the model used when predicting testing data produces RMSE of 0.1448.
Creator
Yuslena Sari, Eka Setya Wijaya, Andreyan Rizky Baskara, Rico Silas Dwi Kasanda
Source
DOI: 10.12928/TELKOMNIKA.v18i2.14826
Publisher
Universitas Ahmad Dahlan
Date
April 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Yuslena Sari, Eka Setya Wijaya, Andreyan Rizky Baskara, Rico Silas Dwi Kasanda, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
PSO optimization on backpropagation for fish catch production prediction,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3705.
PSO optimization on backpropagation for fish catch production prediction,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3705.