Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 367–372 (2017)
Abstract. The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active users such as Indonesia. Extraction of information from social media can yield insightful results if done correctly. Recent studies have managed to leverage associations between language and personality and build a personality prediction system based on those associations. The current study attempts to build a personality prediction system based on a Twitter user's information for Bahasa Indonesia, the native language of Indonesia. The personality prediction system is built on Support Vector Machine and XGBoost trained with 329 instances. Evaluation results using 10-fold cross validation shows that the system managed to reach highest average accuracy of 76.2310\% with Support Vector Machine and 97.9962\% with XGBoost.
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