<![CDATA[TThis study aims to cluster laptop price data using the K-Means algorithm to obtain a more structured and interpretable price segmentation. The data used in this study consist of laptop price data that have undergone a preprocessing stage, including data cleaning, data transformation, and data normalization to ensure optimal data quality. The determination of the optimal number of clusters was conducted using the Elbow Method by analyzing the Within Cluster Sum of Squares (WCSS) values, which indicated that the optimal number of clusters is k = 3. Subsequently, the K-Means algorithm was applied to group the laptop price data into three clusters based on price characteristics. The clustering results reveal three main groups, namely low-price, mid-price, and high-price laptop clusters. This segmentation provides a clear overview of the laptop market conditions and highlights the differences in price ranges among the clusters. The results demonstrate that the K-Means algorithm is able to cluster laptop price data effectively and consistently. The resulting segmentation can be utilized as a basis for decision-making for consumers as well as business stakeholders in developing marketing strategies and determining appropriate laptop pricing]]>
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