Implementation Of Decision Tree Algorithms For Classification Of Respiratory Infectious Diseases - Fauzi; Taghfirul Azhima Yoga Siswa; Fendy Yulianto (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
Acute Respiratory Infection (ARI) is a common respiratory illness that frequently affects children, primarily caused by viruses such as rhinovirus or adenovirus. In Indonesia, a total of 200,000 ARI cases were recorded during the 2021–2023 period. This study aims to implement the Decision Tree algorithm to classify ARI cases. The dataset consists of 1,501 patient records obtained from UPT Puskesmas Bontang Barat for the 2024–2025 period. The research process includes the pre-processing stage, data splitting into training and testing sets using the 10-Fold Cross Validation technique. Subsequently, model evaluation is conducted using the Confusion Matrix to calculate the Accuracy, Precision, Recall, and F1-Score metrics. The results show that the Decision Tree algorithm is capable of performing classification with good performance, achieving an average accuracy of 81.75%, precision of 79.58%, recall of 81.75%, and an F1-score of 80.45%.