TY - JOUR
T1 - Software development to predict the level of paddy production using Gaussian Copula Marginal Regression
AU - Goldestan, Gandes
AU - Sutikno,
AU - Setiyono, Budi
AU - Mukhlash, Imam
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/5/31
Y1 - 2019/5/31
N2 - Appropriate decision making in rice production management is needed to support national food security efforts, especially in East Java which is the largest rice production province in Indonesia. This study aims to develop a web-based decision support system to predict rice production levels in five districts of rice production centers in East Java. A web-based decision support system is constructed to make information accessible and understandable. The method used in this study is Gaussian Copula Marginal Regression (GCMR), which is regression based on Copula. Predictor variables (rainfall) and response variables (harvested area) are identified their correlation using Copula correlation. Estimation of harvested area is constructed using GCMR model. The results showed that the GCMR model used was able to model the area of rice harvest in five districts of rice production centers in East Java. Furthermore, the system is also able to predict the level of rice production in the short term as decision support to help the authorities give consideration in taking policy related to agriculture and food security in East Java.
AB - Appropriate decision making in rice production management is needed to support national food security efforts, especially in East Java which is the largest rice production province in Indonesia. This study aims to develop a web-based decision support system to predict rice production levels in five districts of rice production centers in East Java. A web-based decision support system is constructed to make information accessible and understandable. The method used in this study is Gaussian Copula Marginal Regression (GCMR), which is regression based on Copula. Predictor variables (rainfall) and response variables (harvested area) are identified their correlation using Copula correlation. Estimation of harvested area is constructed using GCMR model. The results showed that the GCMR model used was able to model the area of rice harvest in five districts of rice production centers in East Java. Furthermore, the system is also able to predict the level of rice production in the short term as decision support to help the authorities give consideration in taking policy related to agriculture and food security in East Java.
UR - http://www.scopus.com/inward/record.url?scp=85067800529&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1218/1/012047
DO - 10.1088/1742-6596/1218/1/012047
M3 - Conference article
AN - SCOPUS:85067800529
SN - 1742-6588
VL - 1218
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012047
T2 - 3rd International Conference on Mathematics; Pure, Applied and Computation, ICoMPAC 2018
Y2 - 20 October 2018
ER -