Data Mining on Women's Clothing Sales in Market Places  with the K-Means Clustering Algorithm

Rizna Fitriana Dalimunthe; Raissa Amanda Putri

doi:10.24014/ijaidm.v7i2.31384

Data Mining on Women's Clothing Sales in Market Places with the K-Means Clustering Algorithm

Rizna Fitriana Dalimunthe, Raissa Amanda Putri

Abstract

Clothing is a necessity that must be used to cover the body with the main material made of fiber or textile so that the body is completely covered without gaps. Marketplace is an application or website that provides online buying and selling facilities from various sources. On the Shopee marketplace, there are many shops selling women's clothing from various groups and types of clothing. The K-Means Clustering algorithm in the research was applied to make it easier for sellers and buyers to find out what kind of women's clothing is currently selling well in the marketplace by grouping it into 3 clusters, namely the best-selling, best-selling, and least-selling. Research data was obtained from the Shopee marketplace with 3 variables, namely product price, number of sales, and buyer assessments of 4 types of women's clothing in the form of tunics, dresses, abayas/gamis, and shirts totaling 1200 data. The results of this research make it easier for buyers to make decisions and sellers to develop shop ideas.

Keywords

Data Mining, Women's Clothing, Marketplace, K-means Clustering

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References

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.31384

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