Pembentukan Pola Peminjaman Buku Pada Perpustakaan Dengan Menerapkan Metode CART dan Normalisasi Z-Score - Ahyuna, A; Lasena, Marlin; Aminuddin, Rosihan; Ardimansyah, Ardimansyah; Azhar, Zulfi (Building of Informatics, Technology and Science (BITS), 2024)
The library is a service place that is useful as a place to borrow all kinds of books to read or as reference material in the form of images, text or other forms. In simplifying book borrowing strategies, the library utilizes realistic borrowing data as an object for strategy discovery by exploring knowledge that can provide information to simplify the book borrowing system. Data Mining can be interpreted as data mining, where in the data mining process a data mining process is carried out which aims to find important, valuable and useful information in a very large collection of databases. If the data held is very large, it is necessary to carry out a preliminary process as a stage to help simplify the process carried out in data mining, or this process is known as data normalization. One method that can be used in the process of normalizing data is the Z-Score Normalization method. The Z-Score Normalization method is a process in the preprocessing stage by decomposing numerical attribute data which can change the values ​​in the data into a certain range. The Z-Score Normalization method itself can also be combined with other methods such as the CART method. The CART method is a method used to carry out the classification process in data mining. The classification process carried out using the CART method is based on the formation of a decision tree with the binary values ​​obtained. The CART method itself is a method used to assist in processing all types of data such as continuous, ordinal, nominal and other data. The results obtained from the Cart Method research can be applied to obtain book borrowing patterns or book association relationships in libraries in the form of rules which can provide important information about book borrowing patterns in the library