Evaluasi Random Forest dan SMOTE dalam Menangani Ketidakseimbangan Kelas pada Klasifikasi Chronic Kidney Disease
-
Rahmat Dwi Cahyono
*
Universitas Ahmad Dahlan, Yogyakarta,
Indonesia
- Herman Yuliasnyah Universitas Ahmad Dahlan, Yogyakarta, Indonesia
- Sunardi Sunardi Universitas Ahmad Dahlan, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
Chronic Kidney Disease (CKD) is a chronic condition characterized by a gradual decline in kidney function and is often detected at an advanced stage. Previous studies have reported that the Random Forest algorithm is capable of achieving good performance in CKD classification. However, CKD datasets generally contain numerous clinical variables and potential class imbalance, which may affect the model’s ability to accurately identify CKD patients. Therefore, this study aims to evaluate the performance of the Random Forest algorithm in CKD classification using the Synthetic Minority Over-sampling Technique (SMOTE) to improve prediction quality and support early disease detection. The study was conducted using a medical dataset consisting of 4,000 patient records. The research stages included exploratory data analysis (EDA), data splitting with an 80:20 ratio, SMOTE implementation, Random Forest modeling, and cross-validation. Model performance was evaluated using a confusion matrix and classification report, including accuracy, precision, recall, and F1-score metrics. The experimental results showed that the model with Random Forest algorithm achieved good classification performance, with an accuracy of 95%, precision of 87%, recall of 49%, and an F1-score of 63% for the CKD class. These findings indicate that the Random Forest algorithm has the potential to serve as an alternative approach in the development of clinical decision support systems for early CKD detection.
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