Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping

Mohammad imron, Uswatun Hasanah, Bahrul Humaidi

Abstract


Rizki Barokah Store is one of the stores that every day sell a variety of basic materials of daily necessities such as food, drinks, snacks, toiletries, and so on. However, some problems occur in the Rizki Barokah Store is often a build-up of product stocks that resulted in the product has expired. This is due to an error in making decisions on the product stock. In addition to these problems, with the amount of sales data stored on the database, the store has not done data mining and grouping to know the potential of the product. Whereas data-processing technology can already be done using data mining techniques. To overcome the period of the land, the technique used in data mining with the clustering method using the algorithm K-means. With the use of these techniques, the purpose of this research is to grouping products based on products of interest and less interest, advise on the stock of products, and know the products of interest and less demand.


Article Metrics

Abstract: 3697 Viewers PDF: 2415 Viewers

Keywords


Data mining; K-Means algorithm; Clustering; Stock.

Full Text:

PDF


Refbacks

  • There are currently no refbacks.



Barcode

IJIIS: International Journal of Informatics and Information Systems

ISSN:2579-7069 (Online)
Organized by:Departement of Information System, Universitas Amikom Purwokerto, IndonesiaFaculty of Computing and Information Science, Ain Shams University, Cairo, Egypt
Website:www.ijiis.org
Email:husniteja@uinjkt.ac.id (publication issues)
  taqwa@amikompurwokerto.ac.id (managing editor)
  contact@ijiis.org (technical & paper handling issues)

 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0