Implementasi Hyperparameter Tuning Grid Search CV Pada Prediksi Produksi Padi Menggunakan Algoritma Linear Regresi

Rice is one of the main crops in Indonesia that produces the largest staple food, namely rice commodities. Rice is a staple food consumed by almost 98% of Indonesian people. This study aims to compare the Linear Regression Algorithm and Decision Tree in an effort to find the most appropriate algorithm for predicting rice production data. Linear Regression is still a useful model, especially if the data has a non- linear relationship that cannot be captured by Linear Regression. So it can be concluded that the Linear Regression Algorithm with optimization of the tuning grid search cv hyperparameter is able to predict rice production better than the Decision Tree Algorithm with an R2-score value of 86.895666, MAE 261049.168107, and MSE 160199780301.226318.