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This research explores the use of Text Mining Methods as an innovative approach to extract important information from research report text. With a strong conceptual foundation from the literature review, this research details the key concepts of Text Mining, such as tokenization techniques, sentiment analysis, and entity extraction. The steps of applying Text Mining Methods to research reports are explained in depth, with a focus on using such techniques to improve the efficiency and accuracy of information extraction. Through the evaluation of the method's performance, the research demonstrates a significant improvement in analysis speed and information extraction accuracy compared to conventional methods. The research conclusions provide a holistic picture of the potential of Text Mining Methods in improving the effectiveness of the research report text analysis process. The implications of this research stimulate thoughts on the applicability of this technology in various disciplines that rely on research reports as a primary source of information. Thus, this research makes a positive contribution to the understanding and development of text analysis techniques to support more efficient decision making.