Improving the Accuracy of Text Classification using Stemming Method, A Case of Informal Indonesian Conversation

Abstract As social beings, humans always interact with one another using either verbal or non-verbal language. Language is an arbitrary sound-symbol system, which is used by members of a community to cooperate, interact, and identify themselves. Indonesian language is classified into two categories, namely formal and non-formal. The former meets the grammatical standard as prescribed by linguistic rules of the language, while the latter tends to deviate it. In daily communication, however, non-formal language is more intensively used because they are more practical and easier to understand. With this tendency, non-formal language causes problems in linguistic computation because most linguistic computations use formal standard languages that already have standardized rules. This research aims to develop a dynamic Indonesian closed corpus related to airline ticket reservation, namely "Incorbiz". The "Incorbiz" will be used as stemming tool for formal and non-formal Indonesian. Text processing, text normalization, and auto-update data were proposed in this research. This research also compared two stemming techniques i.e. "Sastrawi" and "Incorbiz" to process the 30-sample dataset. The algorithm used to process the classification is Support Vector Machine (SVM). The data used to develop the "Incorbiz" were taken from conversations between customer service staff and consumers in airline ticket reservations. The result showed that "Incorbiz" had higher accuracy than "Sastrawi" on 0.89 and 0.67, respectively.