Clustering Data Persediaan Barang Menggunakan Metode Elbow dan DBSCAN

 (*)Trisia Intan Berliana Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Elvia Budianita (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Alwis Nazir (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Fitri Insani (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: December 7, 2023; Published: December 26, 2023

Abstract

In the world of business and inventory management, efficient inventory management is very important. If a company does not have inventory, it is impossible to fulfill consumer desires. Managing inventory requires careful inventory management and good data analysis. Challenges in inventory involve unpredictable fluctuations in demand, making it difficult to determine optimal inventory levels. Product diversification with various characteristics is also an obstacle, hindering grouping and formulating inventory management strategies. The lack of clear product segmentation adds to the inhibiting factor, making it difficult to identify groups of similar goods. Inefficient stockpiling can be detrimental to the business as a whole, so implementing clustering is necessary to optimize inventory strategies based on product characteristics. By analyzing product groups, companies can develop more efficient and effective inventory management strategies. This research uses a clustering method using the elbow method and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The elbow method is used to determine the most optimal EPS and Minpts values. The aim of this research is to group goods inventory data using the attributes Initial quantity (initial stock), quantity sold (stock sold), and quantity available (available product stock). So that grouped data can make it easier for companies to optimize the inventory of the most sold goods. and fans. Based on the elbow and DBSCAN test results, 144 clusters and 0 noise data were obtained, with cluster 2 being the product with the largest number of sales and inventory. The DBSCAN method which was tested without using elbows obtained cluster 3 results and 959 noise data.

Keywords


Inventory; Clustering; Data Mining; Elbow; DBSCAN

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