Application of XGBoost for Risk Level Classification of Fires in Surabaya City in 2024 and Interactive Spatial Visualization Based on Streamlit
DOI:
https://doi.org/10.33005/jasid.v1i2.24Keywords:
fire, risk classification, XGBoost, SMOTE, response time, interactive dashboardAbstract
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.
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