JTIM : Jurnal Teknologi Informasi dan Multimedia
Vol 1 No 1 (2019): May

Kajian dan Rumusan Projek Infrastruktur Jaringan pada Industri Hospitality

Akbar Juliansyah (Universitas Bumigora)
Dyah Susilowati (Universitas Bumigora)
Muhammad Yunus (Politeknik Negeri Jember)

Article Info

Publish Date
14 May 2019


The Hospitality industry is increasingly improving its services to be able to grow as a tourism sector grows. The indication of the growth of the industry can be seen from the rise of international chains that enter or participate in developing their business in Indonesia. In principle, the Chain Hotel is a group of Hotels that is a hotel chain with services that are equal after star hotel equivalence. For that hotel owners who will join a chain of hotels that have been formulated by the chain hotels and usually refer to standards that apply globally / globally. The terms of information technology, chain hotels have implemented standards that follow World standards. This is a technology that is leading to the information system. This is a concern of every hospital industry player to implement. Thus this research is expected to provide infrastructure standards for hotels, especially according to the appropriate ANSI / TIA / EIA standards. The Research Methodology used a continuous development improvement cycle where the cycle is formed into 4 stages, namely Research Formulation and Requirements Analysis, Problem Design and Solutions, Analysis and Discussion of Formulation of Solutions and Suggestions in Framework of Continuous Improvement. The results of this study formulate a template and the contents of a network infrastructure project document in the form of a structured caling system (SCS) and ANSITIA /EIA

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Journal Info





Computer Science & IT


Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, ...