Analisis Perbandingan Algoritma Decision Tree, Random Forest, dan XGBoost untuk Klasifikasi Penyakit Infeksi Gigi dan Mulut

Bernadus Anggo Seno Aji, Yohanes Setiawan, Sukma Dewi Anggraini

Abstract

Oral and dental health are indicators of overall body health. Several factors contribute to dental and oral diseases, such as smoking, alcohol consumption, and excessive intake of sugary foods. Untreated dental diseases can lead to dental and oral infections. These infections may cause various complications, making proper treatment essential. This study aims to develop a classification model for dental and oral infections to assist in early diagnosis. The methods used in this research are tree-based algorithms, including Decision Tree, Random Forest, and XGBoost. Tree-based methods are among the algorithms suitable for categorical input data. The classification results using these methods achieved accuracies of 87.5%, 91.7% and 93.1% without SMOTE and 88.9%, 93.1% and 97.2%.. with SMOTE for handling class imbalance. The best-performing model in this study is XGBoost with SMOTE-applied training data.

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DOI: https://doi.org/10.31284/j.integer.2024.v10i1.7501

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