Penerapan Algoritma XGBoost dalam Klasifikasi Jumlah Korban Kecelakaan Kereta Api di Indonesia

Abstract

This study aims to classify the number of vehicle accident casualties caused by railway accidents in Indonesia into low, medium, and high-risk categories using the XGBoost algorithm, as well as to evaluate the model performance based on accuracy, precision, and recall metrics. The employed methodology is CRISP-DM, consisting of stages such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The dataset was obtained from official reports of the National Transportation Safety Committee (KNKT) and online news articles from 1991 to early 2025, resulting in 112 valid records after preprocessing, including data labeling, transformation of nominal attributes, and conversion of date data into numerical form. The classification process was carried out using RapidMiner. The results show that the XGBoost model achieved an accuracy of 88.39%, with the highest precision and recall values in the low-risk class (0.91 and 0.94) and high-risk class (0.88 and 0.87), while the performance for the medium-risk class remains relatively low (precision 0.75 and recall 0.68), indicating potential data imbalance or insufficient discriminative features. Based on these findings, it can be concluded that the XGBoost algorithm is effective in classifying railway accident risk levels; however, improvements in data quality and feature selection are still needed to achieve more optimal performance.