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Enhancing Indonesian Scientific Article Management through Machine Learning and NLP
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
The exponential growth of scientific literature in Indonesia necessitates efficient automated systems for organizing, retrieving, and assessing the originality of scholarly articles. This paper explores the application of computational methods, specifically machine learning algorithms for classification and similarity measures, to enhance the management of Indonesian scientific journal articles. We investigate the effectiveness of Naive Bayes and Support Vector Machine (SVM) algorithms for thematic categorization and employ Cosine Similarity for identifying content proximity. The proposed framework includes data preprocessing, feature extraction using TF-IDF, and rigorous evaluation of the models. The findings demonstrate the viability of these approaches in improving the accessibility, discoverability, and integrity of the burgeoning volume of Indonesian academic publications. The Naive Bayes method, when applied to a balanced dataset, achieved an impressive F1-score of 98%, indicating high classification accuracy, with the classification process taking less than 60 minutes. Article similarity detection using the Cosine Similarity method accurately reflected the degree of similarity between concatenated titles and abstracts. This research offers a robust framework for enhancing the classification and search capabilities within national aggregator services like Garba Rujukan Digital (GARUDA).
Keywords
References
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