Software development to predict the level of paddy production using Gaussian Copula Marginal Regression

Gandes Goldestan, Sutikno, Budi Setiyono, Imam Mukhlash

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Article number012047
JournalJournal of Physics: Conference Series
Volume1218
Issue number1
DOIs
Publication statusPublished - 31 May 2019
Event3rd International Conference on Mathematics; Pure, Applied and Computation, ICoMPAC 2018 - Surabaya, Indonesia
Duration: 20 Oct 2018 → …

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