Application of XGBoost for Risk Level Classification of Fires in Surabaya City in 2024 and Interactive Spatial Visualization Based on Streamlit - Sarah, Sarah Aprilia Hasibuan; Divia, Divia Prisillia Prisca; Dila, Annita Fadhilah Aprilia; Arman, Dwi Arman Prasetya; Prisma, Prismahardi Aji Riyantoko (Jurnal Aplikasi Sains Data, 2025)
Fire in urban areas such as Surabaya City is a non-natural disaster that can have a significant impact on public safety, economic stability, and the environment. This study aims to develop a fire risk level classification model using Extreme Gradient Boosting (XGBoost) algorithm based on selected predictor variables, namely response time, fire subtype, and number of victims affected. The dataset consists of 859 fire events throughout 2024, enriched with spatial and demographic attributes. The research methodology involved data preprocessing (including label coding and normalization), class imbalance handling with Synthetic Minority Over-sampling Technique (SMOTE), model training with XGBoost, and evaluation using metrics such as accuracy, precision, recall, and f1-score. The classification model achieved excellent performance, with an overall accuracy of 1.00% and perfect precision, recall, and f1-score of 1.00 across all risk categories (low, medium, and high). Confusion matrix and ROC curve analysis confirmed the high predictive ability of this model. In addition, the results were visualized using a Streamlit-based interactive dashboard to enhance the usability of the model for decision-making. These findings highlight the potential of XGBoost as a powerful tool for fire risk classification and emphasize its relevance in supporting early warning systems and evidence-based disaster mitigation policies in urban environments.