Journal Prediction System Of Poor Population In Lubuklinggau City Using Multiple Linear Regression Method - Andri Anto Andri; Budi Santoso (International Journal Cister, 2023)
There are several factors that greatly affect poverty, namely the Human Development Index (IPM), economic growth, and unemployment. There are various forecasting or prediction methods that can be used to predict the poverty rate in the future. One method that is commonly used is the multiple linear regression method. Multiple Linear Regression is a linear regression model involving more than one independent variable or predictor. By forecasting using multiple linear regression models, it is hoped that the government will be able to obtain accurate information about the number of poor people in the future. From the results of the study, it was found that the data error rate obtained MSE (Mean Squere Error) = 114672.764, RMSE (Root Mean Squere Error) = 338.633673146356 and MAPE (Mean Absolute Percentage Error) = 1.227198678. With a mape value of 1.227198678 or 1.227198678%, the value obtained is in the Very Good category