ASSOCIATION RULE MINING ON LIBRARY BOOKS LENDING DATA USING APRIORI AND JACCARD SIMILARITY
Abstract: UPT Perpustakaan UNS
has 37,271 collections and on average 75,316 annual circulations of the book
that is managed by UNSLA (UNS Library Automation). An analysis is needed to
discover valuable information that can be used for various purposes.
Association rule mining is one of data mining techniques to look for
relationship pattern in the market basket data. Apriori algorithm is commonly
used in association rule mining. However, Apriori has limitations in conducting
association rule mining on sparse data. Jaccard Similarity algorithm is used to
find the similarities between the two sets. Application of Jaccard Similarity
to the association rule mining can find association rule on sparse data. This
research was conducted to determine the consistency of association rule
generated by the combination of both Apriori and Jaccard Similarity compared to
regular Apriori and Jaccard Similarity on the book lending data of UPT Library
UNS. Data are grouped into ten different categories of books and split by month
and year. Association rule mining is done by using all three methods.
Association rules produced by each method compared for consistency in the known
month and year. As a result, it is known that the association rule mining using
a combination of Apriori and Jaccard Similarity is more consistent than the
original Apriori and Jaccard Similarity. However, association rule mining using
Jaccard Similarity generate more variation than Apriori and combination. UPT
Perpustakaan UNS has 37,271 collections and on average 75,316annual
circulations of the book that is managed by UNSLA (UNS LibraryAutomation). An
analysis is needed to discover valuable information that can beused for various
purposes. Association rule mining is one of data mining techniquesto look for
relationship pattern in the market basket data. Apriori algorithm iscommonly
used in association rule mining. However, Apriori has limitations inconducting
association rule mining on sparse data. Jaccard Similarity algorithm isused to
find the similarities between the two sets. Application of Jaccard Similarityto
the association rule mining can find association rule on sparse data. This
researchwas conducted to determine the consistency of association rule
generated by thecombination of both Apriori and Jaccard Similarity compared to
regular Aprioriand Jaccard Similarity on the book lending data of UPT Library
UNS. Data aregrouped into ten different categories of books and split by month
and year.Association rule mining is done by using all three methods.
Association rulesproduced by each method compared for consistency in the known
month and year.As a result, it is known that the association rule mining using
a combination ofApriori and Jaccard Similarity is more consistent than the
original Apriori andJaccard Similarity. However, association rule mining using
Jaccard Similaritygenerate more variation than Apriori and combination.
Penulis: Muhammad Hezby Al
Haq, Ristu Saptono, Sarngadi Palgunadi
Kode Jurnal: jptinformatikadd160507
