PENGELOMPOKAN PENJUALAN PRODUK DENGAN MENGGUNAKAN K-MEANS CLUSTERING : STUDI KASUS ANALISIS PENJUALAN COFFEE SHOP OLEH KAGGLE.COM

PENGELOMPOKAN PENJUALAN PRODUK DENGAN MENGGUNAKAN K-MEANS CLUSTERING : STUDI KASUS ANALISIS PENJUALAN COFFEE SHOP OLEH KAGGLE.COM

K-Means Clustering, Data Mining, Product Sales, Data Analysis, Silhouette Score

The purpose of this study is to categorize coffee shop items sold by analyzing sales records with the KMeans clustering method. The Kaggle dataset consists of three main features: Product_ID, number of items
sold (Transaction_Qty), and price per item (unit_price). This method was chosen because of its ability to
identify sales patterns in grouping products into three clusters, namely cluster 0 has 56 products with the
highest sales, cluster 1 has 23 products with medium sales, cluster 2 has 1 product with low sales. This
study involved collecting data, cleaning it, standardizing it, finding out the best number of clusters, and
then checking the results with a silhouette score of 0.67. The findings show that the K-Means method helps create products that are useful for business decisions, and also improves data-driven stock management and marketing plans.

Sifa Rismawati, Shofa Shofiah Hilabi , Bayu Priyatna, Agustia Hananto

https://ojs.itb-ad.ac.id/index.php/JUSIN/article/view/3078

Institut Teknologi dan Bisnis Ahmad Dahlan

2025-06-25

Sri Wahyuni

E-ISSN : 2797-8516

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Indonesian

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