Penerapan Algoritma Naive Bayes Untuk Prediksi Penyakit Paru-Paru Pada Sumber Kaggle Menggunakan Aplikasi Rapid Miner - Saputra, Suwanda Aditya (JATISI, 2024)
The development of information technology is very rapid and has been used in many fields, one of which is the health sector. The development of information technology has a very significant role in treating diseases, one of which is lung disease. In this research, the researcher took the data source from Kaggle. The dataset used can be accessed via the link https://www.kaggle.com/datasets/andot03bsrc/dataset-predic-terkena-penyakit-paruparu and data processing uses the Naive Bayes method with the Rapid Miner supporting application. The amount of training data is 80% and the amount of test data is 20% of the prediction results for each class of accuracy, recall and precision in each target class. Performance Vector also informs the number of true positive values, 2499 data, true negative 403 data, false positive 371 data, false negative 2727 data. In the Vector performance we can see that the resulting accuracy is 87.10%, the resulting Class Recall is 87.09% and the resulting class precision is 87.06. The accuracy prediction results 87.10% show good performance in predicting a number of positive cases of lung disease.