PADDY WETLAND PRODUCTIVITY ANALYSIS WITH LINEAR REGRESSION OF MACHINE LEARNING APPROACH

Abstract

Paddy is one of the priority crops in agricultural production. South Kalimantan is an area that produces Paddy. In paddy productivity in the southern Kalimantan region, there are paddy wetlands and paddy dryland. The need for paddy production in the southern Kalimantan region can increase or decrease every year. The method used in this study is a linear regression algorithm with a machine learning approach. Linear regression analysis basically predicts a variable's value based on its free variables. Linear regression only predicts variables whose data nature is intervals or ratios. Linear regression analysis can be used to examine the relationship between two or more variables. Linear regression can also make additional assumptions between variables through the most suitable lines of straight-line data points. This study is to determine the relationship between harvest area and productivity. As a result of trials using the machine learning approach, linear regression algorithms show a relationship between harvest and production area. The correlation test results can find relationships between data points so that linear regression can be used to predict. From the relationship between harvest area and productivity, a prediction accuracy of 95% was obtained.