Vol 14, No 1 (2010) > Articles >

Ontology-Based Automatic Classification for News Articles in Indonesian Language

Prajna Basnur 1 , Dana Sensuse 1


  1. Fakultas Ilmu Komputer, Universitas Indonesia



Searching specific information will be difficult if only rely on query. Choosing less specific queries will result many irrelevant information fetched by the system. One of the most successful way 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 document classification, such as statistical and machine learning approaches. However, those document classification methods require training data or learning documents. In this study, the authors attempted to classify documents using a method that doesn’t require learning documents. This classification method uses ontology to classify documents. Document classification by 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 showed that the ontology can be used to perform document classification. Recall value is 97.03%, precision is 91.63%, and f-measure is 94.02%.

Keywords: ontology, Naïve-Bayes, stopwords, stemming
Published at: Vol 14, No 1 (2010) pages: 29-35

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