  Internet usage is an important requirement that supports the performance and
activities on campus. To control internet usage, it is necessary to know the
distribution of internet usage. By utilizing a number of machine learning
algorithms and WEKA software, the research is carried out by observation and
taking data from wifi hotspots on campus. The classification method using
SVM-Based utilizes the classification method owned by Support Vector Machine
(SVM). This study aims to classify data on internet usage so that from this
classification can be known destination network, protocol, and bandwidth that
are widely accessed at certain times. Internet traffic data is retrieved
through Wireshark software. Whereas data processing and data processing of
internet traffic is processed by WEKA. The results showed: 1) UBD internet
usage in the week I 133,196 users, week II 304,042 users,2) Use of Destination
Network 24,150 and Use of Protocol 37,321,3) Destination networks that are
often addressed are 172.21.206.143 (the week I) and 172.21.172.234 (week II),
protocols that are often used by TCP, and4) SVM method is a good data mining
method for classifying network packet patterns so as to produce network traffic
classification according to destination network and protocol.
