Implementation Decision Tree Algorithm for Ecommerce Website
DOI:
https://doi.org/10.61841/vyywea86Keywords:
E-Commerce, Dec-Algorithm, Order transactionAbstract
Making e-commerce websites has been widely used in business fields where buyers and sellers don't meet directly; the activities of transactions between one company or individually. The advantages of e-commerce marketing are that the products sold will be more numerous and varied and have a wide range too; shop owners do not need to open another branch to distribute goods. This e-commerce sales system will reduce operational costs because there is no need to use too many employees.The problem today is that on e-commerce websites there will be a very large number of transactions, which are caused by sales data and orders that are opened at any time and not limited by distance and time. This automatically means the amount of data ordering goods must be checked manually. The shop owner will sort and see the product and the number of orders; this is very difficult because of the category of goods sold when they are varied and consist of several product categories. The solution to the above problems can be overcome by adding a product category tracking system feature to the e-commerce website database. By adding the dec-tree algorithm concept, this algorithm will automatically record and read the number of transactions and the number of items sold. The system will then provide a report on the system of the best-selling number of goods and the amount of stock. The results of this report can be used to display a product that is most sold to be offered back to consumers or as a recommendation for the next stock procurement.
Downloads
References
[1] Annisa and Y. Ruldeviyani, Pancarian Target Event Rules dengan metode Pattren Growth, Jakarta: Fakultas ilmu Komputer,Universitas Indonesia, 2007.
[2] Sriandha, R. Elisya, S. M. Ema Rachmawati, and S. M. Intan Nurma Yulita, Implementasi Algoritma Frequent Pattern Growth Pada Recommender System, Bandung: Fakultas Informatika Institut Teknologi Telkom, Bandung, 2012.
[3] Hussain, H.I., Kamarudin, F., Thaker, H.M.T., & Salem, M.A. (2019), Artificial Neural Network to Model Managerial Timing Decision: Non-Linear Evidence of Deviation from Target Leverage, International Journal of Computational Intelligence Systems (forthcoming).
[4] Huffman and S. B., learning information extraction patterns from examples, Price Waterhouse Technology Centre, 68 Willow Road, USA.: Menlo Park CA 94025, 2013.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
