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

Sifa, Sifa Rismawati
Shofa Shofiah Hilabi
Bayu Priyatna
Agustia Hananto

Jurnal Sistem Informasi
Vol 6 No 1 (2025): Jurnal Sistem Informasi

This study aims to group sales products in a coffee shop based on transaction data using the K-Means Clustering algorithm. The dataset from Kaggle.com includes the attributes product_id, transaction_qty, and unit_price. This method was chosen because of its ability to identify sales patterns in grouping products into three main clusters including high, medium, and low sales. The research process includes data collection, pre-processing, normalization, determining the optimal number of clusters, to evaluating the results using a Silhouette Score of 0.65. These results indicate that the K-Means method is effective in providing product segmentation that can be used as a basis for making business decisions, in optimizing stock and data-based marketing strategies.