Klasifikasi Anemia pada Data Tidak Seimbang Menggunakan Random Forest, SMOTETomek, dan GridSearchCV
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1215Keywords:
Anemia Diagnosis; Class Imbalance; GridSearchCV; Random Forest; SMOTETomekAbstract
This study applies a Machine Learning approach to support preliminary anemia identification using image-color information and hemoglobin levels. The main issue in the dataset is an imbalanced class composition, where normal records are more numerous than anemia records and may cause the classifier to learn the majority-class pattern more strongly. To address this issue, Random Forest is integrated with SMOTETomek and GridSearchCV. The Kaggle Anemia Diagnosis dataset contains 104 records, four numerical attributes (%Red Pixel, %Green Pixel, %Blue Pixel, and Hb), and one anemia classification target. The original class distribution consists of 78 normal records and 26 anemia records. SMOTETomek is applied to the training data to balance the classes by generating synthetic minority samples and removing Tomek Links, while GridSearchCV searches for the most appropriate Random Forest hyperparameters. Model performance is assessed using 10-Fold Cross Validation and the Wilcoxon Signed-Rank Test. The proposed model obtains 97.09% accuracy, 97.50% precision, 91.67% recall, and 93.24% F1-score. The Wilcoxon p-value of 0.03125 for accuracy and F1-score indicates a significant difference at the 0.05 level. These findings indicate that the integrated Random Forest, SMOTETomek, and GridSearchCV approach improves anemia classification on imbalanced data.
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