Research Article
Radical Detection on Student Knowledge Using Classification Supervised Learning Method
@INPROCEEDINGS{10.4108/eai.30-7-2019.2287744, author={Asantoso Asantoso and F Rodli and R N Sari and S Hadayatullah and A Prasnowo and S Sehman}, title={Radical Detection on Student Knowledge Using Classification Supervised Learning Method}, proceedings={Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia}, publisher={EAI}, proceedings_a={ACHITS}, year={2019}, month={9}, keywords={great game radical knowledge machine learning and system performance}, doi={10.4108/eai.30-7-2019.2287744} }- Asantoso Asantoso
F Rodli
R N Sari
S Hadayatullah
A Prasnowo
S Sehman
Year: 2019
Radical Detection on Student Knowledge Using Classification Supervised Learning Method
ACHITS
EAI
DOI: 10.4108/eai.30-7-2019.2287744
Abstract
The soft computing used to detect radicalism is the Great Game which contains humanity's psychology programming modules.The output in the form of game assessment are points obtained on contents as a table with the number in column and rows. Machine learning also used for data processing that utilizes SVM method. System performance consists of: learning data to find the nationalism class, training process to find the radicalism class, testing data process using test data already trained and the final process is testing data process using test data before trained. The SVM process results in the form of graphs and information that will be used to recommend whether including nationalism or radicalism.
Copyright © 2019–2024 EAI