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Journal : Zeta - Math Journal

Penerapan Algoritma DBSCAN Untuk Clustering Penjualan di Supermarket Hapsari, Rinci Kembang; Audrey, Talitha Naifa; Widodo, Muhammad Amiruddin; Islamiyah, Mitha
Zeta - Math Journal Vol 9 No 2 (2024): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2024.9.2.83-89

Abstract

Supermarkets are shopping places that provide various daily necessities. Many customers visit supermarkets to buy necessities. The growth of supermarkets is increasing. Supermarkets have a variety of products with different brands, branches, and types of customers. To create a sales strategy, need to know the products that customers are interested in. In this research, supermarket product clustering was carried out based on sales data. The clustering algorithm used in this research is the DBSCAN algorithm. This algorithm is an algorithm for grouping data objects based on density, which is influenced by input parameters, namely the Eps and MinPts values. The data used in this research is secondary data consisting of 100 supermarket sales data, taking 2 attributes. The clustering results show that using the Eps parameter value = 6 and the MinPts value = 9, the product data is divided into 3 clusters, namely cluster 1 of products that are not in demand, cluster 2 of products that are in demand and cluster 3 of very popular products.
Implementation of the K-Means Method for Beverage Clustering Based on Calorie and Protein Rewina, Anggita Eka; Hapsari, Rinci Kembang; Putri, Chatarina Natassya; Lande, Gamaliel Virani Fofid; Aditya, Andre Fransisco; Alamsyah, Mochamad Tegar Bagas
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.19-29

Abstract

Recently, the number of coffee shops in big cities in Indonesia has increased. This makes it easier for coffee lovers to enjoy it. With the increasing public awareness of the importance of healthy drinking patterns in preventing diabetes and other diseases, consuming low-calorie drinks has become a prominent trend. This study aims to group the coffee drink menu at Starbucks based on the calorie and protein content of Starbucks drinks. It is grouped into 2 clusters, namely, high and low clusters. In this study, the clustering process of Starbucks drink menu data was carried out by applying the K-Means algorithm. The clustering results can identify members of Cluster 1 and members of Cluster 2. From the tests that have been carried out, it can group the drink menu into 2 clusters based on the amount of protein and calories from Starbucks drinks and help the public choose which drinks are better to consume.