<![CDATA[The preparation of Cost Budget Plans (RAB) for electrical installation projects is still commonly performed manually using Microsoft Excel, making cable requirement calculations and Unit Price Analysis (AHS) time-consuming and prone to errors. This study develops a machine learning-based system to predict the cable length required for lighting installations using room characteristics, including the number of rooms, floor area, room height, and the number of spotlight, downlight, and pendant lighting points. Two prediction models, Random Forest Regressor and Artificial Neural Network (ANN), were trained using historical residential electrical installation project data with an 80:20 trainingtesting split. Model performance was evaluated using R², MAE, MSE, RMSE, and MAPE. The Random Forest Regressor achieved superior performance with an R² of 0.982 and a MAPE of 18.94%, outperforming the ANN (R² = 0.942; MAPE = 19.34%). The best model was integrated into a Streamlit-based web application to support faster, more accurate, and consistent cable estimation for efficient AHS and RAB preparation.]]>
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