<![CDATA[This study aims to classify the number of malaria cases in Asahan Regency using the Random Forest method. This method was chosen because it is able to handle data with many and complex variables and reduce the risk of overfitting. Data were collected from the Asahan Regency Health Office. The research stages include data collection, preprocessing, model training, and model evaluation. The dataset used consists of 568 malaria case data from 25 sub-districts. The data is divided into 80% for training and 20% for testing. Of the total data, there are 109 data 19.2% in the low category, 334 data 58.8% in the medium category, and 125 data 22.0% in the high category. This classification aims to assist in mapping the level of malaria risk in the area. In this study, several variables were used for model training, including health centers, sub-districts, age, month, and gender. The results of the analysis showed that the most influential variables were health centers 47.53%, followed by sub-districts 43.77%, age 6.07%, months 2.18%, and gender 0.45%. The Random Forest model built was evaluated using accuracy, precision, recall, and F1-Score metrics. The evaluation results showed that the model was able to classify the number of malaria cases well, with an accuracy value of 0.97. With these results, Random Forest has proven effective as a classification method in malaria cases in Asahan Regency.]]>
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