ANALISIS POLA PEMINJAMAN BUKU PERPUSTAKAAN UNIVERSITAS RIAU MENGGUNAKAN ALGORITMA FREQUENT PATTERN GROWTH (FP-GROWTH)
| dc.contributor.author | Erlita, Maya | |
| dc.contributor.supervisor | Risanto, Joko | |
| dc.date.accessioned | 2021-09-30T06:42:36Z | |
| dc.date.available | 2021-09-30T06:42:36Z | |
| dc.date.issued | 2021-02 | |
| dc.description.abstract | Riau University Library provides a variety of services for its visitors. One of the services provided is book loan transaction service. Every day in the library there will be dozens of book loan transactions and in general the transaction data is only used as an archive without being used to generate information or patterns of book borrowing on a data set. To find the pattern, it is necessary to analyze the data of book loan transactions. One of the analyses that can be used is by data mining method of association rules using algorithms (FP-Growth). In implementing FP-Growth method, we used data from book loan transactions in Library of Riau University which consist of 18721 transactions and the parameters used are minimum support 40% and minimum confidence 75%. FPGrowth completion stages are to form FP-Tree and then fp-tree mining process by forming conditional pattern base, forming FP-Tree and forming frequently pattern growth. From these stages, the results obtained were 102 rules with several combinations of items, namely a combination of 2 items, a combination of 3 items and a combination of 4 items. In conclusion, the rules produced in this study are influenced by the minimum value of support and minimum confidence, the greater the value, the more accurate the rules will be. | en_US |
| dc.description.sponsorship | Riau University Library provides a variety of services for its visitors. One of the services provided is book loan transaction service. Every day in the library there will be dozens of book loan transactions and in general the transaction data is only used as an archive without being used to generate information or patterns of book borrowing on a data set. To find the pattern, it is necessary to analyze the data of book loan transactions. One of the analyses that can be used is by data mining method of association rules using algorithms (FP-Growth). In implementing FP-Growth method, we used data from book loan transactions in Library of Riau University which consist of 18721 transactions and the parameters used are minimum support 40% and minimum confidence 75%. FPGrowth completion stages are to form FP-Tree and then fp-tree mining process by forming conditional pattern base, forming FP-Tree and forming frequently pattern growth. From these stages, the results obtained were 102 rules with several combinations of items, namely a combination of 2 items, a combination of 3 items and a combination of 4 items. In conclusion, the rules produced in this study are influenced by the minimum value of support and minimum confidence, the greater the value, the more accurate the rules will be. | en_US |
| dc.identifier.other | wahyu sari yeni | |
| dc.identifier.uri | https://repository.unri.ac.id/handle/123456789/10241 | |
| dc.language.iso | en | en_US |
| dc.subject | Library | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Book Lending Patterns | en_US |
| dc.subject | FP-Growth | en_US |
| dc.title | ANALISIS POLA PEMINJAMAN BUKU PERPUSTAKAAN UNIVERSITAS RIAU MENGGUNAKAN ALGORITMA FREQUENT PATTERN GROWTH (FP-GROWTH) | en_US |
| dc.type | Article | en_US |