Penerapan Data Mining untuk Mengolah Data Penempatan Buku Perpustakaan di SMP Negeri 2 Wewewa Barat dengan Algoritma Apriori

Adriana Yulita Bili(1*), Yulius Nahak Tetik(2), Karolus Wulla Rato(3),

(1) STIMIKOM Stella Maris Sumba, Indonesia
(2) STIMIKOM Stella Maris Sumba, Indonesia
(3) STIMIKOM Stella Maris Sumba, Indonesia
(*) Corresponding Author

Abstract


A school library is a unit in educational institutions that plays an important role as a source of knowledge for anyone or can also be said to be a supporting facility in increasing insight and knowledge for students. The many book collections in the library will certainly make it difficult for visitors (students) who are registered as library members to find the books or literature they want to read. Therefore, it is necessary to carry out a good and structured book placement technique according to each category. The technique used by the author in this research consists of observation, and interviews. At the observation stage in this research, the author carried out a direct inspection of the library at SMPN 2 Wewewa Barat to see directly the activities or management and placement of books, while in the interview, the author collected the results of the interview. with the Head of the library, namely Sister Magdalena, CSV. This interview process was carried out to obtain direct information about the book placement patterns that have been implemented in the West Wewewa 2 Middle School library. The results of the interview with the head of the library were used as primary and secondary data. From the data obtained, an analysis of the problems currently occurring in the library is then carried out to become a reference in modeling a structured book placement system. Apart from the interview data collected, transaction data was also collected during observations carried out directly in the library. Based on the results of the research including implementation, discussion, and testing of the system that has been described, it can be concluded that the application of algorithms using data mining techniques is very efficient in speeding up the process of forming tendencies towards combinations of transaction data set items and the results of the algorithm testing process used to produce association rules that are formed. from the combination of items that meet the minimum support, namely 3% and minimum confidence 7% and have the 2 highest item sets with support of 83.33%

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DOI: https://doi.org/10.30645/kesatria.v4i4.266

DOI (PDF): https://doi.org/10.30645/kesatria.v4i4.266.g264

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