Data mining has been widely used to diagnose diseases from medical data. Classification is a data mining technique that can be used to predict disease. In previous studies, a support vector machine was widely used to obtain high accuracy in predicting hepatitis. In this study, the principal component analysis was applied to the support vector machine. A principal component analysis is used to extract features and reduce the number of features or attributes. Principal component analysis can reduce data dimensions without removing important information from the dataset. The extracted and reduced data are then used to classify the support vector machine. Classification performance measurement is done by using a confusion matrix. Hepatitis prediction accuracy achieved was 93.55%. This result is better than the support vector machine classification results without the application of principal component analysis.
Serangan terhadap server jaringan dapat terjadi kapan saja, jenis serangan yang dapat menyebabkan efek yang signifikan pada sebuah router adalah UDP-Flooding. UDP (User Datagram Protocol)-Flooding adalah jenis serangan yang memanfaatkan protokol UDP dengan mengurangi sambungan (connectionless) untuk menyerang target. Dalam analisis ini menggunakan metode penelitian deskriptif untuk memperoleh data secara langsung dengan melakukan teknik flooding serta teknik pencegahannya terhadap server yang telah dirancang. Dengan menggunakan Filter Rules yang telah dibuat, packet yang melalui port DNS selain IP Address yang telah di allow jika mencoba melakukan request atau flood DNS ke IP Public ISP pada router mikrotik, maka packet tersebut akan langsung di drop oleh pengaturan rules tersebut. kesimpulan yang dapat diambil yaitu penerapan filter firewall pada router mikrotik dapat mengurangi jumlah paket data UDP yang dikirimkan oleh attacker melalui port DNS sebanyak 60% dari jumlah paket yang masuk jika tanpa firewall.
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