Klasifikasi Tipe Anemia berdasarkan Parameter CBC menggunakan Metode SVM - Gilang Febrianto; Erliyan Redy Susanto (Dinamik, 2026)
The precision medicine paradigm is shifting conventional diagnosis toward AI-based systems. However, the literature on Support Vector Machines (SVM) with systematic hyperparameter optimization for the multi-class classification of anemia (Iron Deficiency/ID, Iron Deficiency Anemia/IDA, Normal) remains limited. This study develops an optimized SVM model for identifying three categories of anemia using a Kaggle dataset (1,000 samples, 6 features: Hemoglobin, RDW, MCV, Age, Gender, Anemia Type). The methods include data preprocessing, an 80:20 stratified split, and hyperparameter optimization via 5-fold cross-validation (CV) grid search with exploration of kernels (linear, RBF, poly) and the C parameter (0.1, 1, 10, 100). Results show that an SVM with a linear kernel and C=100 achieves 100% accuracy on the test data, with perfect precision, recall, F1-score, and ROC AUC (1.000) for all classes, as well as a cross-validation mean accuracy of 99% (±1%). Feature analysis identifies Hemoglobin as the dominant predictor, followed by RDW, MCV, Age, and Gender. The study’s contributions include an SVM benchmark framework for anemia classification, a demonstration of the effectiveness of hyperparameter optimization, and the strengthening of the Indonesian medical informatics literature in the application of machine learning to metabolic diseases, which accelerates the digital transformation of clinical hematology practice.