IMPLEMENTASI SISTEM AUTOMATIC TEXT SUMMARIZATION BERBASIS FITUR DAN METODE JARINGAN SYARAF TIRUAN PROPAGASI BALIK

Muhammad Sulthan Syaddad(1*), Mohammad Syafrullah(2)

(1) 
(2) 
(*) Corresponding Author

Abstract


In the era of Industry 4.0, information has become a primary necessity for society today as it enables people to know about various current events worldwide. With the rapid development of information technology and the internet, there has been an abundance of documents available that we can search according to our needs. Text Summarization Machines have the function of presenting essential information from the original documents in a shorter format while still preserving the main content and helping users understand the information from lengthy documents faster. In this case, the method used is the Text Summarization Feature-Based approach, utilizing the Backpropagation Artificial Neural Network algorithm for sentence prediction calculations. The Backpropagation Artificial Neural Network algorithm seeks the most optimal weights during its process. In the testing process with five document samples, the final result obtained was a text summary model that could predict the overall number of labels correctly. However, it struggled in predicting which ones should be labeled as "true" and which ones should be labeled as "false".

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References


D. W. Brata and A. Hetami, “Perancangan Information Retrieval (IR) Untuk Pencarian Ide Pokok Teks Artikel Berbahasa Inggris Dengan Pembobotan Vector Space Model,” J. Ilm. Teknol. Inf. Asia, vol. 9, no. 1, pp. 53–59, 2015.

Waseemullah et al., “A Novel Approach for Semantic Extractive Text Summarization,” MDPL, pp. 1–14, 2022.

D. Delvin, D. Arisandi, and T. Sutrisno, “APLIKASI PERINGKASAN DOKUMEN MENGGUNAKAN METODE MAXIMUM MARGINAL RELEVANCE (MMR),” J. Ilmu Komput. dan Sist. Inf., vol. 10, no. 1, pp. 62–68, Mar. 2022, doi: 10.24912/jiksi.v10i1.17820.

P. M. Sabuna and D. B. Setyohadi, “Summarizing Indonesian text automatically by using sentence scoring and decision tree,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-Janua, no. February, pp. 1–6, 2018, doi: 10.1109/ICITISEE.2017.8285473.

L. Lebanoff, K. Song, and F. Liu, “Adapting the neural encoder-decoder framework from single to multi-document summarization,” Proc. 2018 Conf. Empir. Methods Nat. Lang. Process. EMNLP 2018, pp. 4131–4141, 2018, doi: 10.18653/v1/d18-1446.

C. Slamet, A. R. Atmadja, D. S. Maylawati, R. S. Lestari, W. Darmalaksana, and M. A. Ramdhani, “Automated Text Summarization for Indonesian Article Using Vector Space Model,” IOP Conf. Ser. Mater. Sci. Eng., vol. 288, no. 1, 2018, doi: 10.1088/1757-899X/288/1/012037.

L. Suanmali, N. Salim, and M. S. Binwahlan, “Fuzzy Logic Based Method for Improving Text Summarization,” vol. 2, no. 1, 2009.

D. P. Ismi and F. Ardianto, “Peringkasan Ekstraktif Teks Bahasa Indonesia dengan Pendekatan Unsupervised Menggunakan Metode Clustering,” Cybernetics, vol. 3, no. 02, p. 90, 2020, doi: 10.29406/cbn.v3i02.2290.

A. J. C. Trappey and C. V. Trappey, “An R and D knowledge management method for patent document summarization,” Ind. Manag. Data Syst., vol. 108, no. 2, pp. 245–257, 2008, doi: 10.1108/02635570810847608.

A. Hernández-castañeda, R. A. García-hernández, Y. Ledeneva, and C. E. Millán-hernández, “Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords,” IEEEAccess, vol. 8, p. https://creativecommons.org/licenses/by/4.0/, 2020, doi: 10.1109/ACCESS.2020.2980226.




DOI: http://dx.doi.org/10.30998/faktorexacta.v17i2.20006

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