OPTIMALISASI FP-GROWTH DENGAN TEKNIK PRUNING UNTUK ANALISIS POLA PEMINJAMAN BUKU UPT PERPUSTAKAAN UNISNU JEPARA
DOI:
https://doi.org/10.36080/skanika.v8i1.3333Keywords:
Association rule, Data analyst, FP- Growth, LibraryAbstract
In the digital age, libraries continue to adapt to provide access to information and knowledge, support education and research, and preserve cultural heritage. The increasing demand for reading materials along with the development of information technology and digital literacy has led to a surge in the amount of data stored on various information provision platforms including libraries. With the sheer volume of data, the challenges of management, analysis and storage have become increasingly complex. Lack of understanding of readers' preferences, inefficiency in book procurement, and difficulty in determining book layout are problems that arise in library management. Therefore, analyzing circulation data is very important, for example using FP- Growth to find patterns of book borrowing. Items that do not meet the criteria but are still included in the calculation process cause the results to be less significant, but pruning which removes items with low frequency of occurrence can improve the accuracy of the analysis. The results of the FP- Growth calculation reveal a relationship between management and economics books with a support of 27%, Confidence 54%, and Lift 956 which means that the two books have a large influence on each other's occurrence. While pruning the number of rules generated is getting smaller, from 26 to 8, but the rules have a strong relationship.
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References
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