Application of Linear Regression and Random Forest Algorithms in Predicting Human Development Index (HDI) - Mulyati Mulyati; Nur Aynun Siregar; Khairunnisa (Program Studi Ilmu Komputer, Universitas Pakuan, 2026)
The Human Development Index (HDI) is an important indicator for assessing the welfare and quality of life of the population in a region. The different growth of the HDI between regions indicates the need for accurate data-based analysis and prediction. One of them is a predictive analysis technique using the Linear Regression Algorithm and Random Forest. This study compares the two algorithms to predict the Human Development Index based on Expected Years of Schooling, Average Years of Schooling, Life Expectancy and adjusted Per Capita Income. The research stages include data collection, data pre-processing, data analysis and model evaluation. The results show that the use of the K-Fold Cross Validation method with a value of K = 5 produces a more optimal linear regression model compared to the Random Forest model. This is indicated by a higher coefficient of determination (R²) value and lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).