Modeling of Rice Production in Indonesia Using Robust Regression with The Method of Moments (MM) Estimation

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Isnaini Dyah Nugrahani
Yuliana Susanti
Niswatul Qona’ah


Indonesia is an agricultural country with the majority of the people making rice which is then processed product of rice as a staple food. However, in the last few years, rice production in Indonesia has decreased. Rice production data with influencing factors, namely, rice harvest area, land area affected by plant pests (OPT), rainfall, the population in Indonesia have outliers and have residuals that are not normally distributed so that regression analysis with the least-squares method cannot be used to estimate the amount of rice production. A robust regression model with Method of Moments (MM) estimation is used to solve outlier problems and violations of normality assumptions. This study aims to determine the robust MM estimation regression model to estimate rice production in Indonesia and determine the factors that significantly influence. The robust regression model of MM estimation on rice production in Indonesia shows that the increase in the amount of harvested area , the land area is exposed to plant pests (OPT)  and the population  will increase the amount of rice production, while the rainfall will reduce the amount of rice production with a high level of confidence. The variable harvested land area  and the population   has a significant effect on the amount of rice production. Based on the results obtained, it is hoped that there will be policies that consider factors that influence rice production to increase the amount of rice production in Indonesia.

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