The assessment process is an important step in the evaluation, as it underlies the successful evaluation of a project. One solution to make the project assessment more objective is to apply the concept of a Group Decision Support System (GDSS), which in the decision process uses computing. This research tries to implement the concept by building an application for project evaluation and providing recommendations on project performance in local government agencies. The proposed Decision Makers (DMs) are involved: Executives of Government Institutions, Project Management Work Units, Business Process Owner Units, and Communities represented by the DPRD. The computational process uses the Multi-Criteria Decision Making (MCDM) method, and the Copeland scores voting method ranks the project of all DMs. The results of application computing in implementing GDSS and MCDM indicate that the process of determining project rankings will be faster and more accurate.

Limited network resources and the increasing number of internet users in the current digital era have an impact on high traffic which results in decreased access speed to internet services. This is also a problem that occurs at the Indo Global Mandiri University (UIGM) Palembang, causing access to academic services to be slow. The purpose of this research is to identify the types of network traffic patterns which are then carried out by the process of grouping and visualizing these types of traffic. The data in this study were taken in real-time at the UIGM campus. The data obtained is the result of responses which are then extracted. The extraction results are processed using the Support Vector Machine (SVM) method for the process of grouping and visualizing data. The results of this study can distinguish types of traffic based on communication protocols, namely tcp and udp, where the results of the experiment were carried out six times with the results being the first experiment where 99.7% TCP and 0.1% for UDP, the second experiment 97.6% for TCP and 1.1% for UDP , trial three 99.7 % TCP and 0.2% UDP, trial four 97.5% and 1.3% UDP, trial five 99.5 TCP and 02% UDP, and the sixth or final try 97.7% TCP and 1.1% UDP. The data from the use of the SVM method obtained several types of traffic such as games by 0.4%, mail 0.2%, multimedia 0.4% and the web by 82.8% and this research still produces data that the pattern is not yet recognized by 15.5% Keywords : Network Traffic, Classification, Support Vector Mesin