Rancang Bangun Aplikasi Stock Zoning & Kehilangan Barang Berbasis Web dan Android dengan Metode Waterfall (Studi Kasus: PT Aplikanusa Lintasarta)

Authors

  • Eri Dariato Universitas Dian Nusantara, Indonesia
  • Desi Ramayanti Universitas Dian Nusantara, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v1i1.4315

Keywords:

PT.Aplikanusa Lintasarta, Stock Zoning System and Loss of Goods, Waterfall, Unified Modeling Language (UML)

Abstract

PT Aplikanusa Lintasarta is a company engaged in the digital industry, namely as an internet service provider in providing data communication services. In a condition the administration / admin of the company found problems with data at the time of data collection, problems that arose related to the complexity of making monthly reports that contained data duplication, the occurrence of loss of outlet goods, selection of goods that took a long time to make reports. The purpose of this study is to design a web-based and android-based stock zoning application system with the waterfall method so that it is expected to make it easier for admins and technicians in administrative problems, especially in stock zoning and lost goods. The method in this study uses the waterfall model and uses the Unified Modeling Language (UML) to visualize the modeling. The results achieved in this research include searching for goods, which contains data on goods/outlet devices, then there is retrieval of goods where both internal technicians/vendors carry out goods retrieval transactions when handling disturbances, the return menu of goods which is used to input goods/data. the device is returned, installed, unplugged and features if there is a loss which is useful for reporting item/device data in the event of a loss problem.

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Published

30-06-2021

How to Cite

Dariato, E., & Ramayanti, D. (2021). Rancang Bangun Aplikasi Stock Zoning & Kehilangan Barang Berbasis Web dan Android dengan Metode Waterfall (Studi Kasus: PT Aplikanusa Lintasarta). Arcitech: Journal of Computer Science and Artificial Intelligence, 1(1), 41–56. https://doi.org/10.29240/arcitech.v1i1.4315

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