Designing Classification Models of Patron Visits to an Academic Library using Decision Tree

DOI

10.2991/icoemis-19.2019.20How to use a DOI?

Keywords

Decision Tree, Academic Library, Classification Models, Patron Visits

Abstract

Classification models of patron visits in library may help the library to reveal factors that affect patron visit and to predict how frequent a patron visit the library. This research aims to design classification models of patron visit in Library of Universitas Negeri Malang using decision tree model. Data is collected using online and offline surveys. The total number of usable responses are 883, in which 402 of the responses collected through on-line survey and 481 of the responses collected through a direct survey at the library area. The sampling method is a convenience random sampling. The classification model is built using Decision tree model. The model accuracy of the classification model is 87.5%. The result shows that a library customer tends to visit library more often when they have an assignment or need references for their thesis/final project. In contrast, a self-motivated patron tends to rarely visit library. This study finds nine attributes that highly affect the frequency of customer visit to the academic library are semesters, faculty, department, internet service, bag storage, reading rooms, OPAC services, staff services and the last is book collection.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Aisyah Larasati
AU  - Muhammad Farhan
AU  - Puji Rahmawati
AU  - Nabila Azzahra
AU  - Apif Miftahul Hajji
AU  - Anik Nur Handayani
PY  - 2019/11
DA  - 2019/11
TI  - Designing Classification Models of Patron Visits to an Academic Library using Decision Tree
BT  - Proceedings of the 2019 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019)
PB  - Atlantis Press
SP  - 139
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/icoemis-19.2019.20
DO  - 10.2991/icoemis-19.2019.20
ID  - Larasati2019/11
ER  -