Application of K-Means Clustering Method to Analyze Consumer Perceptions of Local Products (Case Study: Aerostreet on Shopee)

Amanda Fauza, Mahdi Mahdi, Musta’inul Abdi

Sari


This study aims to implement the K-Means Clustering algorithm to analyze consumer perceptions of the local brand Aerostreet on the Shopee marketplace. This method is applied to understand how consumer perceptions are reflected in product reviews available on the platform. After designing and testing the system using the K-Means Clustering method, the results show that consumer review data produced two clusters with distinct characteristics in each cluster. Out of 750 comments analyzed from 15 Aerostreet products on Shopee, it was found that positive word groups were more dominant than negative word groups in each cluster. This indicates that public perception of the local Aerostreet brand is generally positive. The study is expected to provide valuable information for stakeholders and those seeking insights into consumer perceptions of local products in the online market.

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