Penerapan Principal Component Analysis untuk Mereduksi Dimensi Data Penerapan Teknologi Informasi dan Komunikasi untuk Pendidikan di Sekolah


Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Submitted: 31-08-2016
Published: 26-12-2016
Section: Research Articles
Fulltext PDF Tell your colleagues Email the author

This study aimed to analyze the most influential variable in the implementation of ICT in schools. Principal component analysis  using linear algebra to reduce the dimension of data with variables that are interconnected into a new set of data with variables that are not related to each other, called the principal component. Principal component is used to save and calculate how much correlation within varian. The ICT data is collected from 50 schools, this data is grouped into five group based on reference domain of ICT for education indicator by UIS 2009. Dataset per group is used as input for principal component analysis algorithm with Matlab R2014a and produce principal component. Principal component analysis produce five variable with the most influence based on their domain, there are mean hour for individual using of ICT in curicculum domain, existence school in internet in infrastructure domain, learner proportion in using computer laboratory for learning in teacher development domain, learner propostion that computer basic skill course in participation domain.


ICT; Principal component analysis; Dataset

  1. Diah Wulandari 
    PUSTEKKOM, Kementerian Pendidikan dan Kebudayaan, Indonesia
  2. Toni Prahasto 
    Universitas Diponegoro
  3. Vincencius Gunawan 
    Universitas Diponegoro
  1. Aoki, H., Kim, J., dan Lee, W., 2013, Propagation & level: Factors
  2. influencing in the ICT composite index at the school level,
  3. Computers & Education 60, 310–324.
  4. Dharmaraj, S., Hossain, MA., Zhari, S., Harn, GL., Ismail, Z., 2006, The Use of Principal Component Analysis and Self-Organizing Map
  5. to Monitor Inhibition of Calcium Oxalate Crystal Growth by Orthosiphon Stamineus Extract, Chemometrics and Intelligent
  6. Laboratory Systems 81, pp. 21 – 28.
  7. Farjo, J.,Assi, RA., Masri, W., Zaraket, F., 2013, Does Principal
  8. Component Analysis Improve Cluster-Based Analysis?, IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops 52, pp. 400-403.
  9. Hussain, A.J., Morgan, S. & Al-Jumeily, D., 2011. How does ICT affect
  10. teachings and learning within school education. Proceedings - 4th
  11. International Conference on Developments in eSystems Engineering, DeSE 2011, pp.250–254.
  12. International Telecommunication Union, 2010, Core ICT Indicator,
  13. Switzerland.
  14. Kemdikbud, 2013. Peraturan Menteri Pendidikan dan Kebudayaan No.
  15. Tahun 2013 tentang Tata Kelola Teknologi Informasi dan Komunikasi di Lingkungan Kemdikbud, pp.1–4.
  16. Sánchez, J., Salinas, Á. & Harris, J., 2011. Education with ICT in South Korea and Chile. International Journal of Educational
  17. Development, 31, pp.126–148.
  18. Shlens, J., A Tutorial on Principal Component Analysis, 2014, Google
  19. Research UNESCO Institute for Statistics, 2009. Guide to Measuring Information and Communication Technologies (ICT) in Education, Canada.
  20. UNESCO Institute for Statistics, 2014. ICT in Education in ASIA : A
  21. comparative analysis of ICT integration and e - readiness in schools across Asia, Canada.