Text document clustering using self organizing map: Theses and dissertations of universitas Indonesia

Yantine Arsita Br Panjaitan, Isti Surjandari Prajitno, Asma Rosyidah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceeding - 2017 3rd International Conference on Science in Information Technology
Subtitle of host publicationTheory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
EditorsRafal Drezewski, Goutam Chakraborty, Shah Nazir, Lala Septem Riza, Ummi Raba'ah Hashim, Aji Prasetyo Wibawa, Yaya Wihardi, Andri Pranolo, Enjun Junaeti, Shi-Jinn Horng, Heui Seok Lim, Leonel Hernandez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-126
Number of pages6
ISBN (Electronic)9781509058662
DOIs
Publication statusPublished - 12 Jan 2018
Event3rd International Conference on Science in Information Technology, ICSITech 2017 - Bandung, Indonesia
Duration: 25 Oct 201726 Oct 2017

Publication series

NameProceeding - 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
Volume2018-January

Conference

Conference3rd International Conference on Science in Information Technology, ICSITech 2017
CountryIndonesia
CityBandung
Period25/10/1726/10/17

Keywords

  • document clustering
  • library management system
  • self-organizing map
  • text mining

Fingerprint Dive into the research topics of 'Text document clustering using self organizing map: Theses and dissertations of universitas Indonesia'. Together they form a unique fingerprint.

Cite this