International Journal on Advanced Science, Engineering and Information Technology, Vol. 11 (2021) No. 2, pages: 501-507, DOI:10.18517/ijaseit.11.2.8878

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

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.

Keywords:

National education standards; UNBK; classification modelling; multi-class random forest.

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