Misclassification Analysis of Elementary School Accreditation Data in Ambon City Using Multivariate Adaptive Regression Spline

Authors

  • Sarah Risambessy
  • Salmon Notje Aulele Universitas Pattimura
  • Ferry Kondo Lembang Universitas Pattimura

DOI:

https://doi.org/10.20956/j.v18i3.19451

Keywords:

Missclassification, Accreditation, GCV, MARS, APER

Abstract

Many classification methods have been developed, one of which is the Multivariate Adaptive Regression Spline (MARS) method. MARS is one of the classification methods in the form of a combination of Recursive Partitioning Regression (RPR) and the spline method that is able to process high-dimensional and large-sized data and process data with continuous or binary response variables. The purpose of this study was to measure the misclassification of elementary school accreditation in Ambon city using the MARS method. This study uses accreditation data with the results of eight components of accreditation in elementary schools that have accreditation A (group 1) and accreditation B (group 2) in Ambon city. To evaluate the classification method used the APER classification error measure. The best classification result from the MARS method is when using a combination of BF=32, MI=3, MO=1 because it produces a minimum Generalized Cross Validation (GCV) of 0.066 and information is obtained that the correct classification data is 181 and the misclassified data is 10. Based on the results of the analysis, the size of the APER classification error is 5.23%, which can be said that the MARS method is good or statistically significant for classifying elementary schools in Ambon City based on their accreditation rating.  

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Published

2022-05-15

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Section

Research Articles