Implementation of TF-IDF and XGBoost Algorithms in Scientific Paper Classification

Sany; Robet; Joni

doi:10.59934/jaiea.v5i1.1647

Authors

Sany STMIK TIME
Robet STMIK Time
Joni STMIK Time

DOI:

https://doi.org/10.59934/jaiea.v5i1.1647

Keywords:

Text Classification, TF-IDF, XGBoost, Scientific Paper, Hyperparameter Tuning

Abstract

The rapid growth of scientific publications in the field of informatics demands an accurate and efficient automated classification system. This study aims to implement TF-IDF as a feature extraction method and XGBoost as a classification model to categorize scientific papers, particularly based on their titles. The dataset consists of 1,000 scientific paper titles in the field of informatics, which were collected and processed using text preprocessing techniques. The XGBoost model was trained using TF-IDF vector representations, and hyperparameter tuning was applied to enhance model performance, focusing on parameters such as learning_rate, max_depth, and n_estimators. Evaluation results show that the developed system achieved an accuracy of 81%, with solid performance in distinguishing between “Computer Science” and “Non-Computer Science” categories. This study demonstrates that the combination of TF-IDF and XGBoost is effective for short-text classification such as scientific titles and has potential for further development in multi-class classification and more complex datasets.

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