BibTex Citation Data :
@article{JSINBIS11803, author = {Diah Wulandari and Toni Prahasto and Vincencius Gunawan}, title = {Penerapan Principal Component Analysis untuk Mereduksi Dimensi Data Penerapan Teknologi Informasi dan Komunikasi untuk Pendidikan di Sekolah}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {6}, number = {2}, year = {2016}, keywords = {ICT; Principal component analysis; Dataset}, abstract = { 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. }, issn = {2502-2377}, pages = {91--96} doi = {10.21456/vol6iss2pp91-96}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/11803} }
Refworks Citation Data :
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.
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