<![CDATA[Anemia is a medical condition characterized by hemoglobin levels or red blood cell counts below normal, which disrupts the distribution of oxygen throughout the body. Early detection and classification of anemia subtypes are crucial for determining appropriate medical treatment. This study was conducted at Cut Meutia Regional General Hospital in North Aceh Regency with the aim of developing a classification model for anemia subtypes using Support Vector Machine (SVM) and Random Forest (RF) algorithms. The research follows the CRISP-DM methodology, which includes business understanding, data exploration, data preparation, modeling, evaluation, and implementation. The dataset consists of medical parameters such as age, gender, diagnosis, and results from Complete Blood Count (CBC) tests. During the data preparation phase, normalization, missing data handling, and data balancing using the SMOTE technique were performed. The tuning process was carried out using the RBF kernel for SVM. Model validity was tested using 5-fold cross-validation. The results showed that the Random Forest algorithm achieved the highest accuracy of 96.94% with a processing time of 18.31 seconds, while SVM reached an accuracy of 92.15% with a processing time of 1.63 seconds. Based on these results, the Random Forest algorithm is considered more effective in classifying anemia subtypes and is recommended for development as a decision support system in the healthcare sector. ]]>
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