TY - GEN
T1 - Classification of campus e-complaint documents using Directed Acyclic Graph Multi-class SVM based on analytic hierarchy process
AU - Cholissodin, Imam
AU - Kurniawati, Maya
AU - Indriati,
AU - Arwani, Issa
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/23
Y1 - 2014/3/23
N2 - E-Complaint documents provide information that can be used to measure or evaluate the services that given by campus to its students, lecturers, staff, and public. Using text classification, the documents can be classified based on its importance and urgency. This classification will be useful for campus to make the services better. Classifying the documents can also make the complaints follow-up from campus become faster than before. This paper discussed Directed Acyclic Graph Support Vector Machine (DAGSVM) method based on Analytic Hierarchy Process (AHP) to classify E-Complaint documents into four classes based on the importance and urgecy. Highest accuracy that is obtained from this research is 82,61% with Sequential Training SVM parameters are λ = 0.5, constant of γ = 0.01, Maxiter = 10, and ε = 0.00001, training data 70%, using stemming, and Gaussian RBF kernel without using AHP weight.
AB - E-Complaint documents provide information that can be used to measure or evaluate the services that given by campus to its students, lecturers, staff, and public. Using text classification, the documents can be classified based on its importance and urgency. This classification will be useful for campus to make the services better. Classifying the documents can also make the complaints follow-up from campus become faster than before. This paper discussed Directed Acyclic Graph Support Vector Machine (DAGSVM) method based on Analytic Hierarchy Process (AHP) to classify E-Complaint documents into four classes based on the importance and urgecy. Highest accuracy that is obtained from this research is 82,61% with Sequential Training SVM parameters are λ = 0.5, constant of γ = 0.01, Maxiter = 10, and ε = 0.00001, training data 70%, using stemming, and Gaussian RBF kernel without using AHP weight.
KW - AHP
KW - DAGSVM
KW - documents classification
KW - E-Complaint
UR - https://www.scopus.com/pages/publications/84946689376
U2 - 10.1109/ICACSIS.2014.7065835
DO - 10.1109/ICACSIS.2014.7065835
M3 - Conference contribution
AN - SCOPUS:84946689376
T3 - Proceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems
SP - 247
EP - 253
BT - Proceedings - ICACSIS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014
Y2 - 18 October 2014 through 19 October 2014
ER -