Road Damage Level Prediction Using Data Mining-Based Regression Method - Pangaribuan, Rina Indah Widya; Puspsari, Ratih (Jurnal ICT : Information and Communication Technologies, 2025)
Road damage is a critical infrastructure issue that significantly affects transportation safety, mobility efficiency, and vehicle operating costs, creating the need for an accurate and reliable prediction system to support timely maintenance planning. This study aims to develop and evaluate a road damage prediction model using a data mining–based linear regression method implemented in the WEKA environment. The dataset consists of 140 entries and includes key predictor variables such as daily vehicle volume, road age, rainfall, pavement type, and drainage quality. The methodology involves data preprocessing, linear regression modeling, and performance evaluation using metrics such as MAE, RMSE, and the correlation coefficient. The results show that the linear regression model demonstrates strong predictive capability, achieving a correlation coefficient of 0.8593, an MAE of 6.7954, and an RMSE of 7.763, with road age and pavement type identified as the most influential predictors. These findings indicate that linear regression is an effective and interpretable approach for modeling road deterioration levels and can be utilized as a decision-support tool in road maintenance planning and infrastructure asset management. Practically, the model provides data-driven insights for local governments and related agencies in optimizing repair scheduling and budget allocation based on predicted damage levels.