JTIM : Jurnal Teknologi Informasi dan Multimedia
Vol 3 No 2 (2021): August

Purwarupa Aplikasi Peramalan Kebutuhan Persediaan Barang Dagang Berbasis Website dengan Semi Average Method

I Gusti Ayu Desi Saryanti (Institut Teknologi dan Bisnis STIKOM Bali)
Rosalia Hadi (Institut Teknologi dan Bisnis STIKOM Bali)
I Gusti Ngurah Ady Kusuma (Institut Teknologi dan Bisnis STIKOM Bali)



Article Info

Publish Date
24 Jul 2021

Abstract

The Point of Sale application has now begun to be used by many trading entrepreneurs. Of course, this is because Point of Sale (POS) can record all trade transaction activities, such as recording sales and recording purchases of inventory items. However, the majority of POS applications are only limited to recording transactions and do not yet have the ability to assist traders in determining the need for merchandise. This study discusses how to build an application that can help traders predict the need for merchandise with one of the forecasting methods, namely the Semi Average Method. The application in this study was built with the waterfall method and is based on a website and utilizes sales reports from the POS application that has been used. Based on the website, users don't have to bother installing this application because it can be accessed via a browser and anytime as long as it is connected to the internet. Testing the application in this study using the Blackbox Testing method shows that this application has 100% functionality working. It is hoped that the application from this research can later be used side by side with the existing POS application so there is no need to make changes to the POS application that is already in use.

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Journal Info

Abbrev

jtim

Publisher

Subject

Computer Science & IT

Description

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, ...