Open AccessThe Application of Non-linear Cubic Regression in Rice Yield Predictions
Author(s) -
Retno Tri Vulandari,
Hendro Wijayanto,
Afan Lathofy
Publication year - 2020
Publication title -
desimal
Language(s) - English
Resource type - Journals
eISSN - 2613-9081
pISSN - 2613-9073
DOI - 10.24042/djm.v3i3.6580
Subject(s) - polynomial regression , linear regression , regression analysis , statistics , regression , mathematics , cubic function , yield (engineering) , polynomial , computer science , algorithm , mathematical analysis , materials science , metallurgy
The rice yields have fluctuated in Wonogiri Regency. This occasion happened in 2016-2018. Therefore, a prediction is needed to know whether rice yields will increase or decrease in the following year. The purpose of this study was to apply the polynomial non-linear regression method of third-degree in predicting rice yields. This study utilized the Unified Modeling Language (UML) as the system design, black-box testing as the functional testing, and MSE testing as the validity testing. The computed data was data of 2016-2018. The results showed that the prediction of 2017-2019 using the harvested area model produced more accurate calculations. The harvested area model produced the same MSE value in manual and application calculations, which were 405433,1349 in 2017, 312677,7798 in 2018, and 171183.6347 in 2019. The polynomial non-linear cubic regression is a solution to predict rice yields. The output of the application is the prediction information for rice yields