<![CDATA[This research investigates and compares the effectiveness of two data grouping methods, namely Fuzzy C-Means (FCM) and K-Means, in grouping books based on borrowing frequency in the SMKN 1 Mandau Library. The data used is a track record of book borrowing during a certain period. This analysis aims to evaluate the most suitable method for grouping books based on their borrowing patterns. The FCM method is used to consider uncertainty in grouping, while K-Means prioritizes certainty in group division. The results of the analysis can provide deeper insight into reading preferences in libraries, as well as help library managers in designing book placement strategies that are more effective and responsive to different borrowing patterns. The results of the comparison between K-Means and Fuzzy C-Means in grouping subjects based on borrowing frequency at the SMKN 1 Mandau Library show that K-Means has an SSE of 162,083, indicating good centralization of data in its clusters by grouping subjects into two clusters separated. On the other hand, Fuzzy C-Means shows a centroid with a value of [116.03, 136.52], indicating a more flexible approach with varying degrees of membership for each cluster, and a cluster pattern similar to K-Means. Although K-Means is slightly faster in execution with 0.0031 seconds compared to Fuzzy C-Means which requires 0.0032 seconds, both show almost the same time efficiency. Overall, Fuzzy C-Means is proven to be more effective based on centroid value evaluation in handling data with different degrees of membership, while both K-Means and Fuzzy C-Means provide consistent clustering results and can be considered according to the needs of book lending data analysis in the library of SMKN 1 Mandau.]]>
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