Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Matrik
J. B. Budi Darmawan
Universitas Sanata Dharma
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PREDIKSI SALINITAS AIR LAUT DENGAN DEEP NEURAL NETWORK Wiwien Widyastuti; J. B. Budi Darmawan
Jurnal Ilmiah Matrik Vol 21 No 2 (2019): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v21i2.570

Abstract

Research in the marine field is important to look at the state of the ocean's atmosphere and the biodiversity that can live in it. Based on the CalCOFI data set, seawater salinity correlates with the depth of the sea. Sea water salinity can be predicted based on its depth. The classic method that is often used is the least squares regression. Deep neural network is one of the machine learning methods that has been widely applied to regression problems. This study aims to find better predictive performance by comparing the least squares regression method and the deep neural network method. The research method is done first by making an equation model with the least squares regression method. Second, by training the deep neural network using the same data so that the network model is obtained. Furthermore, the results of both methods are compared by calculating MAE and MSE. The results showed that the network model with superior deep neural network was used to predict data outside the range of trained data compared to the least squares regression method.
Co-Authors Wiwien Widyastuti