<![CDATA[The process of choosing a laptop that suits their needs is often a challenge for consumers because of the variety of specifications and features offered. Many consumers find it difficult to make the right choice, especially because the information available is often not well structured. In addition, each individual's needs vary, ranging from use for daily productivity to special needs such as gaming or graphic design. Therefore, this study aims to develop a prototype design of a laptop recommendation system using the K-Means clustering algorithm, which is able to group laptop specification data into certain clusters based on the similarity of features. A total of 25 laptop specification data were used in this analysis, with the main parameters being RAM capacity and SSD capacity. The data was processed using the data mining method, and the K-Means algorithm was applied to perform grouping. The optimal number of clusters is determined using the elbow method to ensure accurate and relevant results. The results of the grouping show that laptops can be classified into specific groups that represent consumer needs, such as use for daily productivity or high-load work. The prototype design of this system was created using Figma to visualize an intuitive and easy-to-use user interface (UI). With this prototype design, it is hoped that it can be a reference in the development of a system that makes it easier for consumers to choose a laptop that suits their preferences and needs.]]>
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