Skripsi

PENENTUAN POLA PEMINJAMAN BUKU DI PERPUSTAKAAN DENGAN VERIFIKASI HASIL DATA PERHITUNGAN ALGORITMA APRIORI MENGGUNAKAN ALGORITMA EQUIVALENCE CLASS TRANSFORMATION (ECLAT)

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Penilaian

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Since the outbreak of Covid in 2019, library visitors have decreased drastically, resulting in insufficient data and library staff unable to sort books according to visitor criteria on a regular basis as before. Librarians who play a role in book stock must understand book lending transaction activities if they are to improve library visitor services. The application of the Data Mining concept (Data Mining) is expected to assist them in planning and predicting books that are currently popular among visitors from the pattern of borrowing books. By applying association rules to loan transaction data, it will make it easier for officers to process information and search for itemsets. Therefore, this study analyzes data patterns on book borrowing by applying the Apriori and ECLAT Algorithm association methods. The Apriori algorithm describes how two or more objects are related to each other. And the ECLAT Algorithm is a search with the Depth-First Search (DFS) approach in determining the intersection value. Data is prepared by the process of data input, data cleansing, and data transformation into a form that can be processed by the RapidMiner application. Furthermore, the data is processed using the Apriori and ECLAT Algorithms with a minimum Support, Confidence, and Lift ratio of 0.005. The Lift ratio is a value that indicates the validity of the transaction process and provides information whether it is true that book A was borrowed together with book B. Therefore, the combination of books with the highest Lift ratio calculation results is a sign that that combination of books is the book that is borrowed the most often or is currently popular with the visitors.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307004014	T127814	T1278142023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1278142023

Publisher

Indralaya : Prodi Magister Ilmu Komputer, Fakultas Ilmu Komputer, Universitas Sriwijaya., 2023

Collation

vii, 87 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.74 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Data
Prodi Magister Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

ANUG

Other version/related

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