Abstract

Islamic Boarding Schools (pesantren in Indonesian language) often need government funding grants for improving education services, i.e. rehabilitation aid. Many affecting variables such as the number of student, pesantren activity type, and infrastructure condition need further examination, in addition to the large number of institutions. Because of those complex variables and the absence of definite variables pattern about correlation with the target classes, this research proposed two neural network based model for classifying beneficiaries to determine rehabilitation aid for the pesantren institutions and compared which is the best. 15 input variables were used as the features in learning model are accordance with 4 target classes. Neural Network formed from the learning process can generate new data classification as much as 100% for Backpropagation with accuration value 0.5, and 94.45489% for Radial Basis Function with accuration value 0.428571429.

Original languageEnglish
Title of host publication2018 International Conference on Information and Communications Technology, ICOIACT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages454-459
Number of pages6
ISBN (Electronic)9781538609545
DOIs
Publication statusPublished - 26 Apr 2018
Event1st International Conference on Information and Communications Technology, ICOIACT 2018 - Yogyakarta, Indonesia
Duration: 6 Mar 20187 Mar 2018

Publication series

Name2018 International Conference on Information and Communications Technology, ICOIACT 2018
Volume2018-January

Conference

Conference1st International Conference on Information and Communications Technology, ICOIACT 2018
Country/TerritoryIndonesia
CityYogyakarta
Period6/03/187/03/18

Keywords

  • Backpropagation NN
  • Beneficiaries of Islamic Boarding School Rehabilitation Aid
  • Radial Basis Function NN
  • classification

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