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All Journal Jurnal Pseudocode
Ahmad Zidane Arrasyid
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Prediksi Probabilitas Otomatisasi Pekerjaan Tahun 2030 Menggunakan Algoritma Linear Regression Dan Gradient Boosting Nicholas Leonardo; Ahmad Zidane Arrasyid; Natagama, Muhammad Arif Billah; Hafidz Muhammad Dzaky; Vitri Tundjungsari
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.69-74

Abstract

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.
Co-Authors Hafidz Muhammad Dzaky Natagama, Muhammad Arif Billah Nicholas Leonardo Vitri Tundjungsari