Text Classification System Using Text Mining with XGBoost Method

Ni Kadek Dwi Rusjayanthi (Department of Information Technology, Udayana University, Bali, Indonesia)
Anak Agung Kompiang Oka Sudana (Department of Information Technology, Udayana University, Bali, Indonesia)
I Nyoman Prayana Trisna (Department of Information Technology, Udayana University, Bali, Indonesia)

Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi)
Vol 11 No 2 (2023): Vol. 11, No. 2, August 2023

Large data nowadays can be used for analysis; thus, it can obtain important/valuable knowledge in various domains. Text analysis can be carried out by utilizing text mining using computational methods so that knowledge extraction can be carried out on large text data, including processing related to unstructured text data, which is written in natural language. Classification in text mining is a type of work with the searching process for a set of models or functions that describe and differentiate text data classes with the aim that the model can be used to predict the class of an object (text data) whose class is unknown. Text mining was carried out in this research to analyze text data through the Text Classification System using a classification method, namely the XGBoost (eXtreme Gradient Boosting) Method. A text classification system was developed to classify text in the form of articles. The highest accuracy obtained from the test is 77%.