ANALISA KOMBINASI K-NEAREST NEIGHBOR DAN MEAN OF HORNER’S RULE PADA KLASIFIKASI KEYSTROKE DYNAMIC AUTHENTICATION

As time goes by, humans are facilitated in all aspecs of life with increasingly sophisticated technology. But behind it all, there is a threat that cannot be underestimated. The potential for data theft is even a threat. In computer system, users are required to provide a username to be allowed access to systems that are only commonly accessed by the user themselves, such as social media, databases, websites or information systems. Most secure systems need to verify that the user is a real user. One of the steps to provide privacy security is a password. Keystroke dynamic authentication is a biometric method that identifies behaviour based on a person’s typing pattern. Where the information is obtained when the person presses and releases the key on keyboard. This typing pattern can be deifned as a unique characteristic in each person. From the statement above, we try to take advantage of Keystroke Dynamic Authentication by classifying attackers and non-attacakers using the K-Nearest Neighbor method combined with Mean of Horner’s Rul