Keywords
Bandwidth Allocation, Fuzzy Decision Support System, Network Resource Management, Sugeno Methodology, Waterfall Development
Document Type
Article
Abstract
To increase the efficacy of bandwidth allocation at PT. Digdaya Monokrom Group, this study describes the development of a Fuzzy Decision Support System (FDSS) utilizing the Sugeno methodology. The Waterfall development process is employed for the purposes of system planning, construction, and maintenance. The study consists of three primary stages: the creation of fuzzy sets, the development of fuzzy rules, and the process of defuzzification. The study findings demonstrate that the utilization of FDSS has effectively improved the distribution of bandwidth. The distribution has shifted from a uniform one to a more optimized allocation, focusing on the Execution, Content Creator, Administration, and Research Team departments. During a four-week monitoring period, modifications were implemented to distribute bandwidth based on the preferences and needs of various departments, while adhering to the limitations of the current broadband subscription. This has enhanced the efficient exploitation of network resources. The research findings highlight the efficacy of FDSS in prioritizing resource allocations according to specific departmental requirements, consequently improving service quality and maximizing bandwidth subscription capacity. This demonstrates the implementation of strategic management methodologies to optimize the allocation of network resources, resulting in enhanced organizational efficiency and production.
First Page
171
Last Page
181
Page Range
171-181
Issue
2
Volume
8
Digital Object Identifier (DOI)
10.21831/elinvo.v8i2.57557
Source
https://journal.uny.ac.id/index.php/elinvo/article/view/57557
Recommended Citation
I. W. Lahiya and F. Arifin, "Implementation of FDSS (Fuzzy Decision Support System) Sugeno Model in Optimizing Bandwidth Requirement Management of Web-Based Networks,", vol. 8, no. 2, pp. 171 - 181, Dec 2023.
The definitive version is available at https://doi.org/10.21831/elinvo.v8i2.57557
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