FRISKA, FRISKA (2024) BOOK BORROWING ANALYSIS : UNIKA SOEGIJAPRANATA LIBRARY WITH MULTILAYER PERCEPTRON. Skripsi thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.
|
Text
20.K1.0031-FRISKA_COVER_1.pdf Download (147kB) | Preview |
|
![[img]](https://repository.unika.ac.id/style/images/fileicons/text.png) |
Text
20.K1.0031-FRISKA_BAB I_1.pdf Restricted to Registered users only Download (87kB) |
|
|
Text
20.K1.0031-FRISKA_BAB II_1.pdf Restricted to Registered users only Download (164kB) |
|
|
Text
20.K1.0031-FRISKA_BAB III_1.pdf Restricted to Registered users only Download (135kB) |
|
|
Text
20.K1.0031-FRISKA_BAB IV_1.pdf Restricted to Registered users only Download (413kB) |
|
|
Text
20.K1.0031-FRISKA_BAB V_1.pdf Restricted to Registered users only Download (87kB) |
|
|
Text
20.K1.0031-FRISKA_DAPUS_1.pdf Download (147kB) | Preview |
|
|
Text
20.K1.0031-FRISKA_LAMPIRAN_1.pdf Restricted to Registered users only Download (269kB) |
Abstract
The Unika Soegijapranata library plays an important role as an information center and learning resource for students, researchers and the wider community. Despite its function, the library has not conducted comprehensive studies on visitors using algorithmic methods. Therefore, this study aims to analyze and classify the number of library visitors using the multilayer perceptron algorithm. The dataset used consists of 49,668 visitor records from 2019 to 2023. Data processing is performed after preprocessing steps such as data cleaning and feature selection. The multilayer perceptron model is trained and evaluated to produce clustering results based on book borrowing quantities. The results show that the highest book borrowing frequency occurred in 2022, while the lowest was in 2021. The Faculty of Psychology recorded the most book loans. Based on K-Means clustering, borrowers can be divided into three clusters representing different quantities. This research demonstrates the effectiveness of the multilayer perceptron algorithm in analyzing library visitor data. The classification results provide insights into visitor patterns and borrowing behaviors that can help inform operational decision making at the Unika Soegijapranata library.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 004 Data processing & computer science |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mr Yosua Norman Rumondor |
| Date Deposited: | 05 May 2024 12:09 |
| Last Modified: | 05 May 2024 12:09 |
| URI: | http://repository.unika.ac.id/id/eprint/35285 |
Actions (login required)
 |
View Item |