Big Data Labroratory

The IS Study Programme has a specific laboratory namely Lab Big Data whose utilization is devoted to lecturer research activities, students research, and or lecturer workshops. Lab Big Data has 1 computer server with high level specifications, 3 personal computers windows-based, 1 personal computers mac-based, 1 big monitor, 3 LED monitor. Lab Big Data is provided as a medium to improve and develop practical skills of lecturers and students in research, especially research related to data science. For anyone who wants to use the Big Data Lab, they can make a request for scheduling through the portal gapura.umn.ac.id, or directly contact the Lab Big Data coordinator using this link Form Big Data Lab(C503).  Before applying to use Lab Big Data, you can read the Standard Operational Procedure (SOP) first.

General Computer Laboratory

The general computer lab is used for teaching and learning activities that requires hands-on activities. These activities are broadly divided into two parts, namely:
  • Learning activities related to practical courses.
This activities are regular and scheduled every semester. IS Studi Program has 14 compulsory courses with practicum classes where each practicum class requires software that can be used by students in working on practicum modules. To support lab modules, IS study program uses several licensed software such as SAP for courses related to ERP, Oracle for courses related to Database, and Tableau for courses related to Big Data.
  • Additional activities related to training, workshops, and or community service
This activities are unscheduled as they related to additional needs and or individual needs.
Course Software Used
IS203 Applied ERP
IS439 ERP Configuration
IS539 ERP Programming
IS419 Database Administration
IS519 Database Development
IS412 Data Visualization

Academic Tutors

Information Systems students frequently hold some tutorials to support student learning, especially for some courses that are considered difficult for students, namely practical courses such as Algorithm and Data Structure, Data Analysis and Database Systems, Probability and Statistic, etc. This additional learning system will be taught by more senior students who have completed courses related to good grades so that they can be taught again to students at lower levels.