Implementation of the K-Means Clustering Algorithm Based on CRISP-DM Using Orange for Sales Product Grouping in Shopee.

Septi Giseila Putriana; Rika Yani; Pujianto

doi:10.59934/jaiea.v5i3.2478

Authors

Septi Giseila Putriana Universitas Baturaja
Rika Yani Universitas Baturaja
Pujianto Universitas Baturaja

DOI:

https://doi.org/10.59934/jaiea.v5i3.2478

Keywords:

Data Mining, K-Means Clustering, CRISP-DM, Orange, Shopee

Abstract

The growth of e-commerce in Indonesia, especially on Shopee, has generated large product data that are not yet fully used by sellers in decision-making. This study aimed to cluster Shopee product sales data based on physical characteristics and product information completeness using the K-Means Clustering algorithm within the CRISP-DM framework through Orange Data Mining. The dataset included 4,997 product records with eight attributes: product category, product name length, product description length, number of product photos, product weight, and product length, height, and width. After data cleaning, 4,895 records were analyzed. The results identified three clusters C1 contained large and heavy products, C2 contained products with general and dominant characteristics, and C3 contained relatively small to medium products. The silhouette coefficient score was 0.238, indicating a weak cluster structure, but the model still provided an overview of product grouping based on physical characteristics.

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