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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Rena Misliniyati
Departement Geotechnical Hazard Research Unit, Faculty of Engineering, University of Bengkulu, Bengkulu 3871, Indonesia
Computer Science & IT

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Comparative Evaluation of Random Forest and XGBoost for Bearing Capacity Prediction On Slopes Rahmathiyah Amanda; Lindung Zalbuin Mase; Aidil Fitriansyah; Rena Misliniyati; Muharram Nur Fikri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16417

Abstract

Predicting the ultimate bearing capacity of shallow foundations is crucial in geotechnical planning because it is directly related to the safety and stability of the structure. This is especially true for foundations located at the edge of a slope. However, conventional analytical methods often fail to capture the complex and nonlinear relationships between soil and foundation parameters. This study evaluates the performance of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensemble machine learning models for predicting the ultimate bearing capacity (q_ult) of shallow foundations in non-cohesive soils on slopes, with emphasis on methodological rigor and physical model interpretability. The dataset consists of 393 secondary data points obtained from numerical modeling based on the Finite Element Method (FEM) in a previous study. Input variables include slope angle (sin β), foundation width (B), foundation depth (Df), and soil angle of internal friction (tan φ), while ultimate bearing capacity (qult) is used as the output variable. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the A20 index.  Both models achieved a test R² of 0.97. XGBoost demonstrated superior generalization with a test RMSE of 7.130 kN/m² and MAPE of 8.833%, while Random Forest achieved a higher A20 index of 88.136% compared to 86.44% for XGBoost.
Co-Authors Aidil Fitriansyah Lindung Zalbuin Mase Muharram Nur Fikri Rahmathiyah Amanda