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Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education

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@article{IJASEIT8878,
   author = {Aditya Ramadhan and Budi Susetyo and - Indahwati},
   title = {Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {501--507},
   keywords = {National education standards; UNBK; classification modelling; multi-class random forest.},
   abstract = {National Education Standards (SNP) is the minimum criteria that must be met by the education units and/or educational organizations to realize high-quality national education. The evaluation is implemented through accreditation, and national evaluation of graduate competencies carried out through national examination (UN). Research on the causality relationship between SNP and the UN has been done, but research using classification modelling to explain the relationship between SNP and the UN has never been done. This study employed random forest for multi-class classification to examine important variables in improving the quality of education at the high school level (SMA/MA) based on computer-based national exam (UNBK) scores and accreditation results. The highest classification accuracy and G-Mean value were obtained in multi-class random forest modelling of 88.17% and 48.95% based on the evaluation model. This model generates important factors in the classifying the quality of education by the items of accreditation instruments. Important factors are items 69, 68, 62, 71, 67, 55, 56, 83, 45, 39, 36, 33, 64, 46, and 14. Based on the indicators of important factors, SNP has an important role in classifying the quality of education, which are standards of school facilities (SSP), standards of teacher and education staff (SPT), and standards of graduate competency (SKL). The study results advise region governments and education units to collaborate in improving SSP, SPT, and SKL.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8878},
   doi = {10.18517/ijaseit.11.2.8878}
}

EndNote

%A Ramadhan, Aditya
%A Susetyo, Budi
%A Indahwati, -
%D 2021
%T Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education
%B 2021
%9 National education standards; UNBK; classification modelling; multi-class random forest.
%! Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education
%K National education standards; UNBK; classification modelling; multi-class random forest.
%X National Education Standards (SNP) is the minimum criteria that must be met by the education units and/or educational organizations to realize high-quality national education. The evaluation is implemented through accreditation, and national evaluation of graduate competencies carried out through national examination (UN). Research on the causality relationship between SNP and the UN has been done, but research using classification modelling to explain the relationship between SNP and the UN has never been done. This study employed random forest for multi-class classification to examine important variables in improving the quality of education at the high school level (SMA/MA) based on computer-based national exam (UNBK) scores and accreditation results. The highest classification accuracy and G-Mean value were obtained in multi-class random forest modelling of 88.17% and 48.95% based on the evaluation model. This model generates important factors in the classifying the quality of education by the items of accreditation instruments. Important factors are items 69, 68, 62, 71, 67, 55, 56, 83, 45, 39, 36, 33, 64, 46, and 14. Based on the indicators of important factors, SNP has an important role in classifying the quality of education, which are standards of school facilities (SSP), standards of teacher and education staff (SPT), and standards of graduate competency (SKL). The study results advise region governments and education units to collaborate in improving SSP, SPT, and SKL.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8878
%R doi:10.18517/ijaseit.11.2.8878
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 2
%@ 2088-5334

IEEE

Aditya Ramadhan,Budi Susetyo and - Indahwati,"Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 501-507, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.8878.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ramadhan, Aditya
AU  - Susetyo, Budi
AU  - Indahwati, -
PY  - 2021
TI  - Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 501
EP  - 507
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - National education standards; UNBK; classification modelling; multi-class random forest.
N2  - National Education Standards (SNP) is the minimum criteria that must be met by the education units and/or educational organizations to realize high-quality national education. The evaluation is implemented through accreditation, and national evaluation of graduate competencies carried out through national examination (UN). Research on the causality relationship between SNP and the UN has been done, but research using classification modelling to explain the relationship between SNP and the UN has never been done. This study employed random forest for multi-class classification to examine important variables in improving the quality of education at the high school level (SMA/MA) based on computer-based national exam (UNBK) scores and accreditation results. The highest classification accuracy and G-Mean value were obtained in multi-class random forest modelling of 88.17% and 48.95% based on the evaluation model. This model generates important factors in the classifying the quality of education by the items of accreditation instruments. Important factors are items 69, 68, 62, 71, 67, 55, 56, 83, 45, 39, 36, 33, 64, 46, and 14. Based on the indicators of important factors, SNP has an important role in classifying the quality of education, which are standards of school facilities (SSP), standards of teacher and education staff (SPT), and standards of graduate competency (SKL). The study results advise region governments and education units to collaborate in improving SSP, SPT, and SKL.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8878
DO  - 10.18517/ijaseit.11.2.8878

RefWorks

RT Journal Article
ID 8878
A1 Ramadhan, Aditya
A1 Susetyo, Budi
A1 Indahwati, -
T1 Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 501
OP 507
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 National education standards; UNBK; classification modelling; multi-class random forest.
AB National Education Standards (SNP) is the minimum criteria that must be met by the education units and/or educational organizations to realize high-quality national education. The evaluation is implemented through accreditation, and national evaluation of graduate competencies carried out through national examination (UN). Research on the causality relationship between SNP and the UN has been done, but research using classification modelling to explain the relationship between SNP and the UN has never been done. This study employed random forest for multi-class classification to examine important variables in improving the quality of education at the high school level (SMA/MA) based on computer-based national exam (UNBK) scores and accreditation results. The highest classification accuracy and G-Mean value were obtained in multi-class random forest modelling of 88.17% and 48.95% based on the evaluation model. This model generates important factors in the classifying the quality of education by the items of accreditation instruments. Important factors are items 69, 68, 62, 71, 67, 55, 56, 83, 45, 39, 36, 33, 64, 46, and 14. Based on the indicators of important factors, SNP has an important role in classifying the quality of education, which are standards of school facilities (SSP), standards of teacher and education staff (SPT), and standards of graduate competency (SKL). The study results advise region governments and education units to collaborate in improving SSP, SPT, and SKL.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8878
DO  - 10.18517/ijaseit.11.2.8878