Implementation of the Random Forest Regression Algorithm for Estimating the Number of Personnel at the Tangerang City Fire Department Call Center

Abstract

This study aims to apply the Random Forest Regression algorithm to estimate the number of personnel required for the Tangerang City Fire Department Call Center, based on historical fire incident data from 2023 to 2025. The research addresses the need for rapid, accurate, and data-driven personnel estimation to support decision-making by personnel. The study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, comprising Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Understanding. The variables utilized include year, time of incident, district, object type, fire severity level, weather, fleet size, and response time, with the number of personnel serving as the target variable. The study compares Random Forest Regression with Linear Regression using MAE, MSE, RMSE, and R² metrics. Test results indicate that Random Forest Regression achieved an MAE of 1.976, MSE of 6.085, RMSE of 2.467, and R² of 0.953, demonstrating superior performance compared to Linear Regression. The model was subsequently implemented in a Flask-based web application to serve as an operational tool for relevant personnel