A Multi-Ontology Approach for Deforestation Issue Analysis in Indonesian Digital Media

Abstract

The increasing volume of online news discussing deforestation has created challenges for automatically identifying and categorizing environmental issues because conventional text classification methods often fail to capture semantic relationships between terms. This study proposes a multi-ontology semantic feature extraction framework that integrates DBpedia, IndoWordNet, and a domain-specific keyword ontology to enrich document representation for Indonesian deforestation issue classification. News articles were preprocessed through case folding, tokenization, stopword removal, and stemming. Semantic scores generated from the three ontology resources were combined to produce semantic feature vectors and initial issue labels, which were subsequently classified using a Support Vector Machine (SVM). To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and class-weight balancing were incorporated during model training. The proposed model was evaluated using 5-fold stratified cross-validation on a dataset of 567 labeled sentences collected from Indonesian online news portals. Experimental results achieved an accuracy of 73.24%, a macro precision of 61.39%, a macro recall of 47.63%, and a macro F1-score of 51.55%. These results indicate that the proposed multi-ontology framework provides richer semantic representations for deforestation issue classification and offers an interpretable approach for supporting large-scale analysis of Indonesian online news.