@article{repository1032,
            year = {2020},
          volume = {1505},
           title = {Epileptic Seizure Detection in EEGs by Using Random Tree Forest,
Na{\"i}ve Bayes and KNN Classification},
           pages = {1--5},
           month = {March},
          number = {1505},
       publisher = {IOP Publishing},
         journal = {Journal of Physics: Conference Series},
            issn = {1742-6596},
        abstract = {Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to
analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbour (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, na{\"i}ve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN
classifier (accuracy: 92.7\%), rather than random tree forest (accuracy: 86.6\%) and na{\"i}ve bayes classifier (accuracy: 55.6\%). Seen from precision performance, KNN Classification also gives the best precision (82.5\%) rather than Na{\"i}ve Bayes classification (25.3\%) and random tree forest (68.2\%). But, for the sensitivity, Na{\"i}ve Bayes classification is the best with 80.3\% sensitivity, compare to KNN 73.2\% and random tree forest (42.2\%). For specificity, KNN classification gives 96.7\% specificity, then random tree forest 95.9\% and Na{\"i}ve bayes 50.4\%. The training time of na{\"i}ve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than na{\"i}ve bayes and random tree forest classification.},
          author = {Lestari, Fauzi P and Haekal, Mohammad and Edison, Rizki Edmi and Fauzy, Fikry Ravi and Khotimah, Siti Nurul and Haryanto, Freddy},
             url = {https://doi.org/10.1088/1742-6596/1505/1/012055}
}