Text Summarization Umpan Balik Pengguna Website SiBayar Pondok Pesantren Sabilurrosyad dengan Metode Bi-LSTM

Sayyed Aamir Hassan, Supriyono Supriyono, Zainal Abidin

Abstract


The SiBayar website is being developed by the Sabilurrosyad Islamic Boarding School to facilitate its administration and management. To improve the functionality of the website, user feedback is needed on the existing features. However, managing and analyzing a large amount of user feedback manually can be a very time-consuming process. Therefore, an automated approach such as text summarization is needed to summarize and analyze the data. This study aims to generate an automated summary of user feedback on the SiBayar website of the Sabilurrosyad Islamic Boarding School using the Bi-Directional Long Short-Term Memory (Bi-LSTM) method, focusing on identifying the best parameters through hyperparameter tuning and evaluating the accuracy in full. The results of the hyperparameter tuning test show that the configuration that provides the best performance is the one using the Nadam algorithm optimization, the number of layers 1 and batch size 1, and the variational dropout with a dropout rate of 0.5. The model summary quality evaluation was performed using the ROUGE metric which showed that the Bi-LSTM model achieved a ROUGE-1 score of 0.6221, a ROUGE-2 score of 0.5462, and a ROUGE-L score of 0.660. Overall, Bi-LSTM model in this study has good performance in summarizing text, but the suitability of word pairs and sequences still needs to be improved for more optimal results.


Full Text:

References


M. Marsum and Abd. W. Syahroni, “EFEKTIFITAS PENGGUNAAN TEKNOLOGI PADA PESANTREN MODERN DALAM MENGHADAPI REVOLUSI INDUSTRI 4.0,” Jurnal Kariman, vol. 8, no. 02, pp. 233–242, Dec. 2020, doi: 10.52185/kariman.v8i02.155.

D. A. Nawangnugraeni, M. Z. Abdillah, and M. Al’Amin, “Implementasi Aplikasi Android untuk Sistem Penjadwalan Kegiatan Pondok Pesantren PDF Walindo,” Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming, vol. 6, no. 1, pp. 1–8, Jan. 2023, doi: 10.30591/japhb.v6i1.3728.

A. Muchasan, Nur Syam, and Anis Humaidi, “Pemanfaatan Teknologi di Pesantren ( Dampak dan Solusi dalam Konteks Pendidikan ),” INOVATIF: Jurnal Penelitian Pendidikan, Agama, dan Kebudayaan, vol. 10, no. 1, pp. 16–33, Feb. 2024, doi: 10.55148/inovatif.v10i1.849.

M. Maimunah and J. Junadi, “IMPLEMENTASI SISTEM INFORMASI AKADEMIK DI PONDOK PESANTREN,” Al-Afkar : Manajemen pendidikan Islam, vol. 11, no. 01, pp. 56–70, Apr. 2023, doi: 10.32520/al-afkar.v11i01.594.

Z. Munawar, “Meningkatkan Kinerja Individu melalui Kritik/Saran menggunakan Recommender System,” TEMATIK, vol. 6, no. 1, pp. 20–38, Jun. 2019, doi: 10.38204/tematik.v6i1.185.

K. Kothari, A. Shah, S. Khara, and H. Prajapati, “A NOVEL APPROACH IN USER REVIEWS ANALYSIS USING TEXT SUMMARIZATION AND SENTIMENT ANALYSIS: SURVEY,” 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:214738395

Y. Yuliska and K. U. Syaliman, “Peringkasan Dokumen Teks Otomatis Berdasarkan Sebuah Kueri Menggunakan Bidirectional Long Short Term Memory Network,” INTECOMS: Journal of Information Technology and Computer Science, vol. 5, no. 2, pp. 65–71, Dec. 2022, doi: 10.31539/intecoms.v5i2.4729.

P. Liashchynskyi and P. Liashchynskyi, “Grid search, random search, genetic algorithm: a big comparison for NAS,” arXiv preprint arXiv:1912.06059, 2019.

S. Varade, E. Sayyed, V. Nagtode, and S. Shinde, “Text Summarization using Extractive and Abstractive Methods,” ITM Web of Conferences, vol. 40, p. 03023, Aug. 2021, doi: 10.1051/itmconf/20214003023.

I. Sutskever, O. Vinyals, and Q. V Le, “Sequence to Sequence Learning with Neural Networks,” ArXiv, vol. abs/1409.3215, 2014, [Online]. Available: https://api.semanticscholar.org/CorpusID:7961699

N. Singh and B. Gaur, “Data Preprocessing: A Step-by-Step Guide for Clean and Usable Data,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 10, no. 2, pp. 1148–1153, Sep. 2019, doi: 10.61841/turcomat.v10i2.14384.

H. Ding et al., “Fewer Truncations Improve Language Modeling,” ArXiv, vol. abs/2404.10830, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:269187631

Y. Wu and Y. Xing, “Efficient Machine Translation with a BiLSTM-Attention Approach,” arXiv preprint arXiv:2410.22335, 2024.

Satvika, V. Thada, and J. Singh, “A Primer on Word Embedding,” 2021, pp. 525–541. doi: 10.1007/978-981-15-8530-2_42.

M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025, 2015.

V. R. Joseph, “Optimal ratio for data splitting,” Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 15, no. 4, pp. 531–538, Aug. 2022, doi: 10.1002/sam.11583.

M. Ranzato, S. Chopra, M. Auli, and W. Zaremba, “Sequence Level Training with Recurrent Neural Networks,” Nov. 2015, [Online]. Available: http://arxiv.org/abs/1511.06732

J. Terven, D. M. Cordova-Esparza, A. Ramirez-Pedraza, E. A. Chavez-Urbiola, and J. A. Romero-Gonzalez, “Loss functions and metrics in deep learning,” arXiv preprint arXiv:2307.02694, 2023.

B. M. Hussein and S. M. Shareef, “An Empirical Study on the Correlation between Early Stopping Patience and Epochs in Deep Learning,” ITM Web of Conferences, vol. 64, p. 01003, Jul. 2024, doi: 10.1051/itmconf/20246401003.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: 10.1016/j.neucom.2020.07.061.

N. Reimers and I. Gurevych, “Optimal hyperparameters for deep lstm-networks for sequence labeling tasks,” arXiv preprint arXiv:1707.06799, 2017.

C.-Y. Lin, “ROUGE: A Package for Automatic Evaluation of Summaries,” in Annual Meeting of the Association for Computational Linguistics, 2004. [Online]. Available: https://api.semanticscholar.org/CorpusID:964287




DOI: https://doi.org/10.30591/jpit.v10i4.8846

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

JPIT INDEXED BY

  
  

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.