MODEL REGRESI LINEAR UNTUK PREDIKSI BIAYA KONSTRUKSI AKIBAT PERUBAHAN GAMBAR AS BUILT DRAWING - Agus Nugroho; Uflahul Maulina (Jurnal Ilmiah Poli Rekayasa, 2025)
Construction projects are inherently complex and highly susceptible to variations in cost due to discrepancies in work volumes across technical design documents such as Detail Engineering Design (DED), Shop Drawing (SD), and As-Built Drawing (AD). These discrepancies often arise from design adjustments, field conditions, or execution errors, which in turn affect budget accuracy and project efficiency. Such inconsistencies in volume are particularly critical in structural works like reinforcement and concrete, as these elements account for a substantial portion of total project costs. When changes in volume occur, the corresponding cost implications must be accurately estimated to maintain financial control and avoid cost overruns. This research aims to quantitatively predict the effect of volume deviations on cost variations using simple linear regression analysis. Data were collected from nine building samples that had complete documentation of DED, SD, and AD. The study focuses on structural work units with measurements in kilograms (kg) and cubic meters (m³). Regression models were assessed based on statistical indicators such as the coefficient of determination (R²), p-value, intercept, and regression coefficients to determine the most reliable model. The results show that every additional 1 kg of reinforcement volume increases the cost by Rp19,303.00, while every additional 1 m³ of concrete adds Rp1,321,597.14 to the project cost. Both models achieved R² = 1.00, indicating perfect predictive accuracy in estimating cost variations due to volume changes. Manual validation confirmed that the predicted values were nearly identical to actual data with a negligible error rate. These results confirm that simple linear regression is a powerful yet practical analytical tool for predicting construction costs. The model developed in this study provides a scientific foundation for improving cost estimation accuracy, supporting effective budget planning, and mitigating financial risks in construction projects.