TY - GEN
T1 - Text document clustering using self organizing map
T2 - 3rd International Conference on Science in Information Technology, ICSITech 2017
AU - Panjaitan, Yantine Arsita Br
AU - Prajitno, Isti Surjandari
AU - Rosyidah, Asma
PY - 2018/1/12
Y1 - 2018/1/12
N2 - Accessibility is a critical aspect to be considered by college library in order to facilitate users in searching library collections. The Library of Universitas Indonesia, as one of Asia's largest library with more than 1,500,000 book collections, should also concern about accessibility to balance its numerous collections. UI-ana collections or works produced by and associated with Universitas Indonesia; in particular theses (undergraduate and graduate theses) and dissertations are one of the largest numbers of collections in Universitas Indonesia's Library. However, the current collection's management system was still based on the submission of the collection in Universitas Indonesia's Library. Since these collections are arranged with no exact criterion, it is harder for users to find theses and dissertations with the same topic. Therefore, management of these collections based on certain criterion is extremely needed to facilitate users in searching these collections. This research aims to determine the categories that can represent theses and dissertations through abstract text mining of each collection in 2005-2015 with a clustering algorithm, namely Self-organizing Map. This study found 139 categories which will be used to classify theses and dissertations of Universitas Indonesia.
AB - Accessibility is a critical aspect to be considered by college library in order to facilitate users in searching library collections. The Library of Universitas Indonesia, as one of Asia's largest library with more than 1,500,000 book collections, should also concern about accessibility to balance its numerous collections. UI-ana collections or works produced by and associated with Universitas Indonesia; in particular theses (undergraduate and graduate theses) and dissertations are one of the largest numbers of collections in Universitas Indonesia's Library. However, the current collection's management system was still based on the submission of the collection in Universitas Indonesia's Library. Since these collections are arranged with no exact criterion, it is harder for users to find theses and dissertations with the same topic. Therefore, management of these collections based on certain criterion is extremely needed to facilitate users in searching these collections. This research aims to determine the categories that can represent theses and dissertations through abstract text mining of each collection in 2005-2015 with a clustering algorithm, namely Self-organizing Map. This study found 139 categories which will be used to classify theses and dissertations of Universitas Indonesia.
KW - document clustering
KW - library management system
KW - self-organizing map
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85046635974&partnerID=8YFLogxK
U2 - 10.1109/ICSITech.2017.8257096
DO - 10.1109/ICSITech.2017.8257096
M3 - Conference contribution
T3 - Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
SP - 121
EP - 126
BT - Proceeding - 2017 3rd International Conference on Science in Information Technology
A2 - Drezewski, Rafal
A2 - Chakraborty, Goutam
A2 - Nazir, Shah
A2 - Riza, Lala Septem
A2 - Hashim, Ummi Raba'ah
A2 - Wibawa, Aji Prasetyo
A2 - Wihardi, Yaya
A2 - Pranolo, Andri
A2 - Junaeti, Enjun
A2 - Horng, Shi-Jinn
A2 - Lim, Heui Seok
A2 - Hernandez, Leonel
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 October 2017 through 26 October 2017
ER -