Implementasi metode K-Means dalam klasterisasi laptop terbaik
Abstract
Clustering is a method used to group data into groups that have similar characteristics. In this context, clustering is used to group laptops based on specifications and price to assist consumers in choosing a laptop that suits their needs. In this study, the K-means clustering method is used to group laptops into several clusters that have similar characteristics. Clustering is done by considering several important factors such as storage, performance, portability, touchscreen display, size, and price. The initial stages of the research involved preprocessing the data, dividing the data into training data and testing data, and processing the K-means algorithm to cluster the features related to the laptop dataset. Model evaluation was carried out using the Elbow method to determine the optimal number of clusters. In this case an analysis was carried out to compare the types of laptops that can be recommended to consumers based on the needs and costs available. Clustering results provide a comparison of laptops based on specifications and prices. Cluster 2 is suitable for those who need large storage, good performance, and affordable prices. Cluster 3 lends itself to portability, a touchscreen display, and good performance at a small size. Cluster 4 is suitable for compact laptops, good performance and affordable prices.
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