Evaluation of Product Sales Data Using Clustering Method and Hierarchical Divisive Clustering at PT.AYN

Ahir Yugo Nugroho  Harahap; Ria Eka  Sari; Heri  Gunawan; Adnan Buyung  Nasution

doi:10.55927/marcopolo.v2i7.10442

Authors

Ahir Yugo Nugroho Harahap Universitas Potensi Utama Medan
Ria Eka Sari Universitas Potensi Utama Medan
Heri Gunawan Universitas Potensi Utama Medan
Adnan Buyung Nasution Universitas Borobudur

DOI:

https://doi.org/10.55927/marcopolo.v2i7.10442

Keywords:

Data Mining, Sales, Hierarchical Divisive Clustering, Purchasing Trends

Abstract

Data Mining, focusing on the Hierarchical Divisive algorithm, can provide a solution for PT.AYN in overcoming the problem of unifying and evaluating sales data, a company that sells various types of disposable tissues. This study aims to identify products that are in demand and less in demand and to group sales data based on company and product type. The results of this study provide valuable insights for evaluating sales data, understanding distributor purchasing trends, and supporting more effective stock planning, shipping, and marketing strategies. Through the application of the clustering method and the Hierarchical Divisive algorithm, this study offers an effective solution to optimize the use of sales data at PT.AYN, which has the potential to be a valuable asset in formulating long-term business strategies. The conclusion of this study is that the Clustering method and the Hierarchical Divisive algorithm can be used to solve problems in grouping sales data based on various factors, including region and product type, and can assist in developing better marketing strategies.

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