Decision Optimization: Internet Data Assistance for Students during Learning from Home
Edy Budiman
Edy Budiman*, Dept of Informatics, Universitas Mulawarman, Samarinda City, Indonesia.
Manuscript received on August 19, 2020. | Revised Manuscript received on September 02, 2020. | Manuscript published on September 10, 2020. | PP: 372-378 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78450991120 | DOI: 10.35940/ijitee.K7845.0991120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The teaching and learning process during the Covid-19 pandemic made it difficult for students to provide internet data packages for access online learning media from home. An internet assistance program policy is provided to support student online learning. In order for the distribution of internet data to students is objective and proportion, for that reason, measuring the amount of data usage and then used in decision making. The method used in measuring data usage is the Drive-test method for incoming and outgoing data bandwidth. As for the analysis of data assistance decision making using the optimization method and the Rank Order Cendroid (ROC) weighting method for each criterion. Measurement results of internet data usage obtained an average value of 407MB-1.0GB per hour with 40 participants in the group. The results of the decision making analysis method for internet data assistance obtained the highest optimization value obtained by 0.40117 with a weighted ROD value of 0.4667. The research study on internet data usage and optimization of decision-making methods is a case study in the Computing classification field: networks and applied computing. Both are implemented in management optimization internet data assistance to students.
Keywords: Data usage, Learning from home, Decision, Students.
Scope of the Article: Machine Learning