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  • 标题:Data science for digital culture improvement in higher education using K-means clustering and text analytics
  • 本地全文:下载
  • 作者:Dian Sa'adillah Maylawati ; Tedi Priatna ; Hamdan Sugilar
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2020
  • 卷号:10
  • 期号:5
  • 页码:4569-4580
  • DOI:10.11591/ijece.v10i5.pp4569-4580
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要:This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
  • 关键词:Clustering;Data science;Digital culture;Higher education;K-means algorithm;Text analytics;Word cloud
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