K-Means and J48 Algorithms to Categorize Student Research Abstracts

Abstract

Text mining is a rapidly growing field in computer science that is used to extract meaningful information from text data. This information can be used for various applications, such as categorizing research abstracts based on their content. This study focuses on the use of text mining techniques. The goal was to determine which algorithm was more accurate in categorizing the research abstracts. The results of the study indicated that the J48 algorithm outperformed the K-Means algorithm in terms of accuracy. This suggests that the J48 algorithm is a more effective method for categorizing research abstracts based on their content. Additionally, the findings provide insight into the use of text mining techniques for categorizing research abstracts in specific fields, such as computer science. Overall, the study demonstrates the potential of text mining techniques for analyzing and categorizing large volumes of text data. As the field of text mining continues to grow, it is likely that more applications will emerge, making it easier to extract valuable information from unstructured text data. The findings of this study can be used to improve the efficiency and accuracy of text mining techniques, particularly for categorizing research abstracts in specific fields.