<![CDATA[The development of Artificial Intelligence (AI) is expected to affect the future employment structure, particularly regarding automation risks in 2030 as a period of accelerated AI adoption across various industrial sectors. This study aims to compare the performance of the Random Forest and Artificial Neural Network (ANN) algorithms in predicting the impact of AI on jobs. The study employed two modeling approaches, namely regression to predict job automation probability and classification to determine job risk categories into Low, Medium, and High classes through a discretization process. The dataset was obtained from Kaggle with a total of 3,000 records and processed through preprocessing, feature engineering, and train-test splitting with an 80:20 ratio. Regression evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²), while classification evaluation used accuracy and F1-score. The results showed that Random Forest achieved the best regression performance with an MAE of 0.0786, RMSE of 0.0932, and R² of 0.8640, outperforming ANN with an MAE of 0.0949, RMSE of 0.1137, and R² of 0.7973. In the classification task, both algorithms achieved an accuracy and F1-score of 99.33%. This study shows that Random Forest is more stable on tabular data and contributes to the comparative analysis of ensemble learning and neural network approaches for predicting the impact of AI on jobs.]]>
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