Keyphrase Extraction Using TextRank for Indonesian Text

Fadel Muhammad, Novi Yusliani, Muhammad Naufal Rachmatullah

Abstract

Keywords are commonly used as a form of summary from scientific publications. But in determining keywords, it requires expertise in the related field and a long amount of time because you have to read and understand the entire contents of scientific publications. Keyphrase Extraction can be a solution to get relevant keywords in a short time based on titles and abstracts from scientific publications. TextRank method is used to extract keywords. This research will perform keyword extraction using the TextRank method for Indonesian text. The evaluation results of this study showed an accuracy value of 95.53% and an f1-score of 59.32% with a threshold configuration of 80% and using all keyword candidates.

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References

Asrori, R. B., Setyawan, R., & Muljono, M. (2020). Performance analysis graphbased keyphrase extraction in Indonesia scientific paper. Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, ISemantic 2020, 185–190. https://doi.org/10.1109/iSemantic50169.2020.9234231.

Mothe, J., Ramiandrisoa, F., Rasolomanana, M., Mothe, J., Ramiandrisoa, F., & Rasolomanana, M. (2020). Automatic keyphrase extraction using graph-based methods To cite this version : HAL Id : hal-02640988.

Abdurrohman. (2018). Evaluasi Algoritma Textrank pada peringkasan teks berbahasa indonesia. 4–16.

Bałcerzak, B., Jaworski, W., & Wierzbicki, A. (2014). Application of textrank algorithm for credibility assessment. Proceedings - 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2014, 1, 451–454. https://doi.org/10.1109/WI-IAT.2014.70.

Diana Permata Sari; Ayu Purwarianti. (2014). Ekstraksi Kata Kunci Otomatis Untuk Dokumen Bahasa Indonesia Studi Kasus: Artikel Jurnal Ilmiah Koleksi Pdii Lipi. Baca: Jurnal Dokumentasi Dan Informasi, 35(2), 139–147. https://doi.org/http://dx.doi.org/10.14203/j.baca.v35i2.192

Pramudita, H. R., Utami, E., & Amborowati, A. (2016). Pengaruh Part of Speech Tagging Berbasis Aturan dan Distribusi Probabilitas Maximum Entropy untuk Bahasa Jawa Krama. Jurnal Buana Informatika, 7(4), 235–244. https://doi.org/10.24002/jbi.v7i4.764.

Dinakaramani, A., Rashel, F., Luthfi, A., & Manurung, R. (2014). Designing an Indonesian part of speech tagset and manually tagged Indonesian corpus. Proceedings of the International Conference on Asian Language Processing 2014, IALP 2014, 66–69. https://doi.org/10.1109/IALP.2014.6973519.

Wicaksono, A. F., & Purwarianti, A. (2010). HMM based part-of-speech tagger for Bahasa Indonesia. In Fourth International MALINDO Workshop, Jakarta. Fourth International MALINDO …, June. https://www.researchgate.net/profile/Alfan-Farizki-Wicaksono/publication/209387036_HMM_Based_Part-of-Speech_Tagger_for_Bahasa_Indonesia/links/04d39ed44efdacf36e3af1a2/HMM-Based-Part-of-Speech-Tagger-for-Bahasa-Indonesia.pdf

Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004 - A Meeting of SIGDAT, a Special Interest Group of the ACL Held in Conjunction with ACL 2004, 85, 404–411.

Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 4(2), 113. https://doi.org/10.26418/jp.v4i2.27526.

DOI: https://doi.org/10.36706/sjia.v5i1.62

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