The Best Problem Transformation Method in Multi-Label Classification Text for Thesis Abstract

The thesis data is increasing yearly; classification is one method that can be used to obtain added information from this case. The classification that suits this case is the multi-label classification. The multi-label classification with the Problem Transformation approach is a flexible approach. In this study, a comparison of the Problem Transformation methods, namely Binary Relevance, Label Powerset, and Classifier Chain, was carried out with the Multinomial Naïve Bayes algorithm as an estimator. As a result, the Classifier Chain method gives results that tend to be better, with the value of Hamming Loss being 0.0321 and an accuracy of 64.60%.