Analisis Clustering Menggunakan Algoritma K-Means Dalam Pengelompokan Penjualan Produk Bahan Bangunan
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Syahriani Syahriani
*
Universitas Bina Sarana Informatika, Jakarta,
Indonesia
- Popon Handayani Universitas Bina Sarana Informatika, Jakarta, Indonesia
- Tri Santoso Universitas Nusa Mandiri, Jakarta, Indonesia (*) Corresponding Author
Abstract
Sandimas Group is a trading airline that is a leading supplier of building materials throughout Indonesia. This building materials trading company markets various granite tile and sanitary products. But unfortunately, this trading company still has a problem, namely that it is still difficult to ensure the quantity of supply of products that customers really like. This problem can be solved with a system that can identify the best-selling products from various product groups. The aim of this research is to find out the right and appropriate product supply so that there is no buildup of product in the warehouse. This can be seen based on the clusters that have been formed. The formation of clusters to group merchandise products in this trading airline uses the K-Means Clustering algorithm. This algorithm can perform calculations accurately. This research is expected to be able to answer this problem correctly based on the similarity of the data. The application of the K-Means Clustering algorithm at the Sandimas Group is by collecting known product supplies in the sales transaction recap for 1 period in 2022. The results of calculating the K-Means Clustering algorithm from 15 known products are 5 products which are included in clusters 1, 6 products are included in cluster 2 and 4 products are included in cluster 3. This cluster can be seen based on the similarity of the data, namely cluster 1 is formed where buyers really like the price in the range of IDR 152,640 to IDR 225,000, cluster 2 is formed where customers quite like the product price in the range of IDR 455,896 to IDR 530,000 while cluster 3 was formed where customers liked product prices ranging from IDR 217,000 to IDR 252,000.
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