KLASIFIKASI DAN DETEKSI KEMIRIPAN ARTIKEL BERBAHASA INDONESIA DI JURNAL BERKALA ILMIAH PADA GARBA RUJUKAN DIGITAL (GARUDA) MENGGUNAKAN METODE NAÏVE BAYES DAN COSINE SIMILARITY

CAHYANI, NYIMAS SABILINA and Stiawan, Deris and Abdiansah, Abdiansah (2025) KLASIFIKASI DAN DETEKSI KEMIRIPAN ARTIKEL BERBAHASA INDONESIA DI JURNAL BERKALA ILMIAH PADA GARBA RUJUKAN DIGITAL (GARUDA) MENGGUNAKAN METODE NAÏVE BAYES DAN COSINE SIMILARITY. Masters thesis, Sriwijaya University.

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Abstract

The classification of articles into several categories has been done using the Naive Bayes method, with an F1-score result of 98% on balanced data based on titles and abstracts. The results show a high level of classification accuracy, with a processing time of less than 60 minutes. Similarity detection between articles was carried out using the Cosine Similarity method, and a similarity score of 0.071 was obtained, reflecting the low similarity level between articles. In this research, the score range used was 0 to 1, where a score close to 1 indicates the highest level of similarity. The search for similar scientific articles was conducted using the Cosine Similarity method based on titles and abstracts, by sorting articles by highest similarity score. The most similar articles are shown in the top order, and the search process takes 44 to 50 seconds to search time. The results of this research show that the method used can increase the accuracy in the classification process, similarity detection, and article search on the Garba Rujukan Digital (GARUDA) platform accurately and efficiently.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Naive Bayes, Cosine Similarity, Classification, Detection
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Nyimas Sabilina Cahyani
Date Deposited:	04 Jul 2025 02:57
Last Modified:	04 Jul 2025 02:57
URI:	http://repository.unsri.ac.id/id/eprint/176552

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