Ontology-Based Automatic Classification for News Articles in Indonesian Language - Basnur, Prajna Wira; Sensuse, Dana Indra (UI Scholars Hub, 2010)
Ontology-Based Automatic Classification for News Articles in Indonesian Language. Searching specific information will be difficult if relying only on query. Choosing less specific queries will result in a lot of irrelevant information fetched by the system. One of the most successful ways to overcome this problem is to perform document classification based on the topic. There are many methods that can be used to conduct such a classification, such as using statistical and machine learning approaches. However, those document classification methods require training the data or learning the documents. In this study, the authors attempted to classify documents using a method that doesn’t require learning the documents. This classification method uses ontology to classify documents. Document classification using ontology is done by comparing the value of similarity among documents and existing node in the ontology. A document is classified into a category or a node if it has the highest similarity value in one of the nodes in the ontology. The results show that ontology can be used to perform document classification. The recall value is 97.03%, the precision is 91.63%, and the f-measure is 94.02%.