Published: 2026-01-01
Optimalisasi Peringkasan Artikel Teks Bahasa Indonesia dengan Kombinasi TextRank dan Graph Neural Network Sederhana
DOI: 10.35870/jtik.v10i1.5247
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Abstract
The delivery of digital information in Indonesian-language news presents challenges in efficiently capturing the core information. This study proposes a combination of the TextRank algorithm and a simple Graph Neural Network (GNN) to improve the quality of automatic text summarization. TextRank is used to construct a sentence graph based on TF-IDF similarity and cosine similarity, followed by training a SimpleGNN model to optimize sentence scores. Evaluations were conducted on 1,000 articles from the Liputan6 dataset using the ROUGE metric (ROUGE-1, ROUGE-2, and ROUGE-L). The results show that this combined method improves performance compared to pure TextRank, especially in capturing semantic relationships between sentences. This study demonstrates that the integration of a simple GNN can enrich representations in graphs and provide more informative and contextual summaries.
Keywords
Automatic Text Summarization ; TextRank ; Graph Neural Network ; SimpleGNN ; ROUGE ; Indonesian ; Liputan6 ; Natural Language Processing
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 10 No. 1 (2026)
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Section: Computer & Communication Science
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Published: %750 %e, %2026
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License: CC BY 4.0
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Copyright: © 2026 Authors
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DOI: 10.35870/jtik.v10i1.5247
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Muhammad Rifqi Syatria
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Dadang Iskandar Mulyana
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
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Aulia, V. (2017). Utilizing mind mapping to summarize English text with the theme “American culture”. Journal of Educational Science and Technology, 3(3), 218-225.
-
Chen, K. Y., Liu, S. H., Chen, B., Wang, H. M., Jan, E. E., Hsu, W. L., & Chen, H. H. (2015). Extractive broadcast news summarization leveraging recurrent neural network language modeling techniques. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(8), 1322-1334. https://doi.org/10.1109/TASLP.2015.2432578.
-
Eris, E., & Pragantha, J. (2017). Penerapan algoritma textrank untuk automatic summarization pada dokumen berbahasa indonesia. Jurnal Ilmu Teknik dan Komputer, 1(1), 71-78.
-
Gao, Q., & Huang, J. (2025). Design and Implementation of Classical Literature Sentiment Analysis System Based on Ensemble Learning and Graph Neural Network. International Journal of Cognitive Computing in Engineering.
-
Gulati, V., Kumar, D., Popescu, D. E., & Hemanth, J. D. (2023). Extractive article summarization using integrated TextRank and BM25+ algorithm. Electronics, 12(2), 372.
-
Hernawan, Y. F., Adikara, P. P., & Wihandika, R. C. (2022). Peringkasan Artikel Berbahasa Indonesia Menggunakan TextRank dengan Pembobotan BM25. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 9(1), 61-68.
-
Hossain, M. M., Anselma, L., & Mazzei, A. (2023, November). Exploring sentiments in summarization: SentiTextRank, an Emotional Variant of TextRank. In Proceedings of the 9th Italian Conference on Computational Linguistics (CLiC-it 2023) (pp. 535-539).
-
Jain, R., Singh, P., & Puri, S. (2023, September). Summarization of Daily News Using TextRank and TF-IDF Algorithm. In Congress on Intelligent Systems (pp. 313-324). Singapore: Springer Nature Singapore.
-
Kazemi, A., Pérez-Rosas, V., & Mihalcea, R. (2020). Biased TextRank: Unsupervised graph-based content extraction. arXiv preprint arXiv:2011.01026. https://doi.org/10.48550/arXiv.2011.01026.
-
Khaliq, A., Awan, S. A., Ahmad, F., Zia, M. A., & Iqbal, M. Z. (2024). Enhanced topic-aware summarization using statistical graph neural networks. Computers, Materials and Continua, 80(2), 3221-3242.
-
Koto, F., Lau, J. H., & Baldwin, T. (2020). Liputan6: A large-scale Indonesian dataset for text summarization. arXiv preprint arXiv:2011.00679. https://doi.org/10.48550/arXiv.2011.00679.
-
Lestari, I. N. T., & Mulyana, D. I. (2022). Implementation of OCR (Optical Character Recognition) using Tesseract in detecting character in quotes text images. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 58-63.
-
Mallick, C., Das, A. K., Dutta, M., Das, A. K., & Sarkar, A. (2018, August). Graph-based text summarization using modified TextRank. In Soft Computing in Data Analytics: Proceedings of International Conference on SCDA 2018 (pp. 137-146). Singapore: Springer Singapore.
-
Mulyana, D. I., Yel, M. B., & Rahmanto, M. D. (2023). Optimasi Penerapan Metode Text Recognition Dalam Fitur Catatan Otomatis Berbasis Mobile. Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 8(2), 489-498.
-
Pan, S., Li, Z., & Dai, J. (2019, May). An improved TextRank keywords extraction algorithm. In Proceedings of the ACM Turing Celebration Conference-China (pp. 1-7).
-
Widyassari, A. P., Rustad, S., Shidik, G. F., Noersasongko, E., Syukur, A., Affandy, A., & Setiadi, D. R. I. M. (2022). Review of automatic text summarization techniques & methods. Journal of King Saud University-Computer and Information Sciences, 34(4), 1029-1046.
-
Wu, L., Cui, P., Pei, J., Zhao, L., & Guo, X. (2022, August). Graph neural networks: foundation, frontiers and applications. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (pp. 4840-4841). https://doi.org/10.1145/3534678.3542609.
-
Yang, F., Zhang, H., Tao, S., & Fan, X. (2024). Simple hierarchical PageRank graph neural networks. Journal of Supercomputing, 80(4).
-
Yang, H., & Gonçalves, T. (2025). MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach. Information, 16(4), 308.
-
Zhang, M., Li, X., Yue, S., & Yang, L. (2020). An empirical study of TextRank for keyword extraction. IEEE access, 8, 178849-178858.
-
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81.
-
Zidan, M. R., & Setiawan, K. (2025). Implementasi Algoritma Rabin-Karp dalam Pendeteksian Plagiarisme pada Dokumen Makalah Mahasiswa. Jurnal Indonesia: Manajemen Informatika dan Komunikasi, 6(1), 273-284. https://doi.org/10.35870/jimik.v6i1.1191.
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