Classification of campus e-complaint documents using Directed Acyclic Graph Multi-class SVM based on analytic hierarchy process

Abstract

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.