Analisis Prediksi Probabilitas Otomatisasi Pekerjaan Tahun 2030 Menggunakan Algoritma Linear Regression Dan Gradient Boosting - Nicholas Leonardo; Ahmad Zidane Arrasyid; Muhammad Arif Billah Natagama; Hafidz Muhammad Dzaky; Vitri Tundjungsari (Jurnal Pseudocode, 2026)
The rapid development of artificial intelligence and automation is expected to significantly impact future employment. This study aims to predict job automation probability in 2030 using supervised learning methods. A public dataset containing job types, education levels, and automation probabilities was utilized. Linear Regression and XGBoost Regressor were employed to build and compare predictive models. The research process included data preprocessing, training–testing data split, model training, and performance evaluation using Root Mean Square Error (RMSE) and coefficient of determination (R²). Experimental results indicate that XGBoost outperforms Linear Regression by achieving lower RMSE and higher R² values. This study provides insights into automation risks and may support workforce skill development planning.