Optimasi AdaBoost dan XGBoost untuk Klasifikasi Obesitas Menggunakan SMOTE - Sukmawati, Cici Emilia; Pratama, Adi Rizky; Hikmayanti, Hanny; Juwita, Ayu Ratna (Politeknik Harapan Bersama, 2025)
Obesity is a condition in which a person's weight exceeds the normal limit due to excessive accumulation of fat tissue. Thus, obesity is considered a global public health challenge. This is evidenced by the latest data from the World Health Organization (WHO) in 2022, namely that 2.5 billion adults aged 18 years and over are overweight and 890 million of them are obese. Therefore, it is very important to accurately identify these risk factors in order to implement effective interventions in the prevention and management of obesity. However, in previous studies there has been no application of SMOTE with the AdaBoost and XGBoost algorithms, so this study aims to compare the performance of the AdaBoost and XGBoost algorithms with SMOTE. The stages of this research begin with problem identification, data collection, preprocessing and model evaluation and model comparison. This study also applies the SMOTE technique to balance unbalanced data. Based on the results of the research that has been carried out, it shows that the accuracy and recall values of the XGBoost algorithm with SMOTE are 0.945 and precision 0.947. Meanwhile, the accuracy and recall values on AdaBoost with SMOTE are 0.388. Then, the precision is 0.371. Thus, it is expected that the results of the XGBoost model with SMOTE can be a source for other research and can help in efforts to prevent and manage obesity.