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Internet usage is an important requirement that supports the performance andactivities on campus. To control internet usage, it is necessary to know thedistribution of internet usage. By utilizing a number of machine learningalgorithms and WEKA software, the research is carried out by observation andtaking data from wifi hotspots on campus. The classification method usingSVM-Based utilizes the classification method owned by Support Vector Machine(SVM). This study aims to classify data on internet usage so that from thisclassification can be known destination network, protocol, and bandwidth thatare widely accessed at certain times. Internet traffic data is retrievedthrough Wireshark software. Whereas data processing and data processing ofinternet traffic is processed by WEKA. The results showed: 1) UBD internetusage in the week I 133,196 users, week II 304,042 users,2) Use of DestinationNetwork 24,150 and Use of Protocol 37,321,3) Destination networks that areoften 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 miningmethod for classifying network packet patterns so as to produce network trafficclassification according to destination network and protocol.

Penerapan Metode SVM-Based Machine Learning Untuk Menganalisa Pengguna  Data Trafik Internet (Studi Kasus Jaringan Internet Wlan Mahasiswa Bina  Darma)