
Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping
Author(s) -
Mohammad Imron,
Uswatun Hasanah,
Bahrul Humaidi
Publication year - 2020
Publication title -
ijiis: international journal of informatics and information systems
Language(s) - English
Resource type - Journals
ISSN - 2579-7069
DOI - 10.47738/ijiis.v3i1.3
Subject(s) - cluster analysis , computer science , product (mathematics) , stock (firearms) , data mining , k means clustering , variety (cybernetics) , database , engineering , mathematics , machine learning , artificial intelligence , mechanical engineering , geometry
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.