Classification Of Malaria Types Using Naïve Bayes Classification - Hadin La Ariandi; Arief Setyanto; Sudarmawan Sudarmawan (Jurnal Indonesia Sosial Teknologi, 2024)
This study was conducted to determine the level of accuracy of the naïve bayes classification method in the process of determining the group type of malaria. This method is used to predict the category of malaria based on the symptoms displayed. In this study, the dataset used was divided into 60% for training and 40% for testing. The results showed that the naïve bayes algorithm had an accuracy rate of 99.8% in predicting malaria categories. Evaluation of model performance using confusion matrix and ROC curve also showed good results, with classification accuracy of 0.998, error 0.002, and AUC 0.999. The results of the classification report show that the Quartana, Tertiana, and Tropica categories are more dominant than the Ovale category, based on precision, recall, and f1-score. These results show that the naïve bayes classification method is effective in classifying types of malaria and can be used as an aid in the diagnosis of malaria.