Cluster Analysis on Laptop Sales Data and Specifications Using K-Means and K-Medoids Methods

This research aims to address the challenges in understanding the relationship between laptop specifications and sales prices and to enhance product segmentation based on cluster analysis. By using available laptop specifications and sales price data, this study aims to identify patterns in laptop specifications that influence sales prices using K-Means and K-Medoids cluster analysis. This research employs the K-Means and K-Medoids clustering methods to categorize laptops into several categories based on specifications such as screen size (inches), price, RAM capacity, and weight. The data transformation process, exploratory analysis, model building, and cluster performance evaluation were conducted using the RapidMiner analysis tool. The research results show that the K-Medoids algorithm provides more accurate clustering performance compared to K-Means, with a Davies-Bouldin Index value of -0.665 for K-Medoids and -0.487 for K-Means at configurations k=4 and k=5. A lower Davies-Bouldin Index value indicates that K-Medoids better represents the characteristics of the existing data. The clustering results identify laptop categories based on a combination of specifications and prices