Prediksi Barang Sering dan Jarang Terjual Dengan Menggunakan Algorithma K-Mean Clustering (Studi Kasus Toko Bina Mulia)

Authors

  • Muhammad khaerudin Universitas Bhayangkara Jakarta Raya
  • Imam Zaenuddin Universitas Bhayangkara Jakarta Raya
  • Tukino Universitas Bhayangkara Jakarta Raya

DOI:

https://doi.org/10.31599/72dwrc41

Keywords:

Clustering, Item Type, K-Means, Rapid Miner

Abstract

The rapid increase in the population in the buffer zone of the capital city has an effect on the lifestyle of the people in the area, including in the Bekasi district. Likewise, the growth of small and medium community businesses, one of which is the Bina Mulia cooperative shop. This study was made to determine which types of goods are often sold and which types of goods are
rarely sold. The algorithm used is K-Means Clustering, where data grouped based on the same characteristics will be included in the same group and the data sets entered into the groups do not overlap. The information displayed is in the form of groups of product names and the amount sold in one week for two months, namely April and May as a sample. The results of this study will help the store in analyzing which types of goods are often sold and which are rarely sold. The software used to help this grouping is Rapid Miner.

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Published

2022-06-30

Issue

Vol. 3 No. 1 (2022): Juni 2022

Section

Artikel

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How to Cite

Prediksi Barang Sering dan Jarang Terjual Dengan Menggunakan Algorithma K-Mean Clustering (Studi Kasus Toko Bina Mulia). (2022). Journal of Informatic and Information Security, 3(1), 1 – 12. https://doi.org/10.31599/72dwrc41