Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Main Article Content

Imam Cholissodin

Abstract

Abstract- Determining a policy is often limited in a short time so that decisions are prone to inaccuracies and are ultimately judged to be less targeted. Therefore, there is a necessity to use data mining technology. Currently, especially due to the continuously increasing case of the spread of COVID-19 in Indonesia, in order to reduce the rate of spread of COVID-19, the government has established a COVID-19 vaccination and emergency Community Activity Restriction Implementation (PPKM) policies. For the success of the policies, the government is required to ascertain and understand the attitude of the society. Hence, the policies can be accepted and supported by the society. YouTube is one of the sources to discover people’s attitudes because in YouTube, people can express their opinions freely. In this study, a model based on Natural Language Processing (NLP) with the Deep Learning method was developed to analyze people’s attitudes from their writings or posts on social media. As for the algorithm stages, first the model analysis was created using the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms as a comparison. In the COVID-19 vaccination policy, the Bi-LSTM algorithm provided a better evaluation value, i.e., the accuracy value of 87.20%, recall of 87.14%, precision of 87.14%, and F1-score of 87.14%. In the emergency PPKM policy, the LSTM algorithm provided a better evaluation value of 95.13%, recall of 93.94%, precision of 94.08%, and F1-score of 94.01%. In addition, using the Maximum Marginal Relevance (MMR) method to obtain recommendations related to the COVID-19 vaccination policy, the result showed that the government needs to carry out re-socialization regarding vaccination and the impact of the vaccine so that the society can become more cooperative and the emergency PPKM policy needs to be reviewed because it has an impact on the society’s economy.

Article Details

How to Cite
CHOLISSODIN, Imam. Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms. Proceedings of the International Conference on Green Technology, [S.l.], v. 11, n. 1, p. 6 - 11, nov. 2021. ISSN 2580-7099. Available at: <http://conferences.uin-malang.ac.id/index.php/ICGT/article/view/1394>. Date accessed: 28 mar. 2024. doi: https://doi.org/10.18860/icgt.v11i1.1394.
Section
Technology Information

References

[1] Wardani, G. W. (2021, 7 24). UPDATE Kasus Corona Indonesia 24 Juli 2021: Tambah 45.416 Positif, 39.767 Sembuh, 1.415 Meninggal. Retrieved 7 24, 2021, from Tribunnews.com: https://www.tribunnews.com/corona/2021/07/24/update-kasus-corona-indonesia-24-juli-2021-tambah-45416-positif-39767-sembuh-1415-meninggal
[2] Anonim. (2021, 7 7). PPKM Darurat: Tugas Bersama Turunkan Pandemi Covid-19. Retrieved 7 25, 2021, from covid19: https://covid19.go.id/p/berita/ppkm-darurat-tugas-bersama-turunkan-pandemi-covid-19
[3] Hidayat, M. (2020, 11 27). Kupas Data: Vaksin Covid-19, Antara Harapan dan Keraguan. Retrieved 7 26, 2021, from Liputan6: https://www.liputan6.com/tekno/read/4410507/kupas-data-vaksin-covid-19-antara-harapan-dan-keraguan
[4] Fathulrahman, A. (2021, 3 23). Survei SMRC: Pro dan Kontra Kebijakan PPKM Mikro Berimbang. Retrieved 7 25, 2021, from mediaindonesia: https://mediaindonesia.com/humaniora/392639/survei-smrc-pro-dan-kontra-kebijakan-ppkm-mikro-berimbang
[5] A. Hassel, “Public Policy,” International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Elsevier, pp.569-575, 2015.
[6] A. Hasan, E.R.M. Putri, H. Susanto, N. Nuraini, “Data-driven modeling and forecasting of COVID-19 outbreak for public policy making,” ISA Transactions, 2021.
[7] Chien-Hsiang Liao, Mu-Yen Chen, “Building social computing system in big data: From the perspective of social network analysis,” Computers in Human Behavior, vol. 101. 2019, pp. 457-465.
[8] Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, Aug. 2013.
[9] Anonim. (2021, 4 5). Yuk Pahami Jenis-jenis Algoritma Deep Learning. Retrieved 7 26, 2021, from dqlab: https://dqlab.id/yuk-pahami-jenis-jenis-algoritma-deep-learning
[10] Wang, M., Lin, T., Jhan, K., & Wu, S. (2021). Abnormal event detection, identification and isolation in nuclear power plants using LSTM networks. Progress In Nuclear Energy, 140, 103928. doi: 10.1016/j.pnucene.2021.103928
[11] Olah, C. 2015. Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/ (10 April 2019).
[12] Fu, Q., Wang, C., & Han, X. (2020). A CNN-LSTM network with attention approach for learning universal sentence representation in embedded system. Microprocessors And Microsystems, 74, 103051. doi: 10.1016/j.micpro.2020.103051
[13] Zhao, S., Cai, Z., Chen, H., Wang, Y., Liu, F., & Liu, A. (2019). Adversarial training based lattice LSTM for Chinese clinical named entity recognition. Journal Of Biomedical Informatics, 99, 103290. doi: 10.1016/j.jbi.2019.103290
[14] Kleenankandy, J., & K A, A. (2020). An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies. Information Processing & Management, 57(6), 102362. doi: 10.1016/j.ipm.2020.102362
[15] Su, J., Dai, Q., Guerin, F., & Zhou, M. (2021). BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. Computer Speech & Language, 67, 101169. doi: 10.1016/j.csl.2020.101169
[16] Behera, R., Jena, M., Rath, S., & Misra, S. (2021). Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management, 58(1), 102435. doi: 10.1016/j.ipm.2020.102435
[17] Cho, M., Ha, J., Park, C., & Park, S. (2020). Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition. Journal Of Biomedical Informatics, 103, 103381. doi: 10.1016/j.jbi.2020.103381
[18] Zhang, Y., Wang, J., & Zhang, X. (2021). Conciseness is better: Recurrent attention LSTM model for document-level sentiment analysis. Neurocomputing, 462, 101-112. doi: 10.1016/j.neucom.2021.07.072
[19] Zhao, L., Xu, W., Gao, S., & Guo, J. (2020). Cross-sentence N-ary relation classification using LSTMs on graph and sequence structures. Knowledge-Based Systems, 207, 106266. doi: 10.1016/j.knosys.2020.106266.
[20] Chen, S., Lang, B., Liu, H., Li, D., & Gao, C. (2021). DNS covert channel detection method using the LSTM model. Computers & Security, 104, 102095. doi: 10.1016/j.cose.2020.102095
[21] Na, S., Kim, H., Min, J., & Kim, K. (2019). Improving LSTM CRFs using character-based compositions for Korean named entity recognition. Computer Speech & Language, 54, 106-121. doi: 10.1016/j.csl.2018.09.005
[22] Zhang, W., Li, Y., & Wang, S. (2019). Learning document representation via topic-enhanced LSTM model. Knowledge-Based Systems, 174, 194-204. doi: 10.1016/j.knosys.2019.03.007
[23] Ring, M., Schlör, D., Wunderlich, S., Landes, D., & Hotho, A. (2021). Malware detection on windows audit logs using LSTMs. Computers & Security, 109, 102389. doi: 10.1016/j.cose.2021.102389
[24] Chen, C., & Dai, J. (2021). Mitigating backdoor attacks in LSTM-based text classification systems by Backdoor Keyword Identification. Neurocomputing, 452, 253-262. doi: 10.1016/j.neucom.2021.04.105
[25] Duan, X., Ying, S., Cheng, H., Yuan, W., & Yin, X. (2021). OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network. Information Systems, 95, 101618. doi: 10.1016/j.is.2020.101618
[26] Zhang, J., Li, K., & Wang, Z. (2021). Parallel-fusion LSTM with synchronous semantic and visual information for image captioning. Journal Of Visual Communication And Image Representation, 75, 103044. doi: 10.1016/j.jvcir.2021.103044
[27] Geng, Z., Chen, G., Han, Y., Lu, G., & Li, F. (2020). Semantic relation extraction using sequential and tree-structured LSTM with attention. Information Sciences, 509, 183-192. doi: 10.1016/j.ins.2019.09.006
[28] Verwimp, L., Van hamme, H., & Wambacq, P. (2020). State gradients for analyzing memory in LSTM language models. Computer Speech & Language, 61, 101034. doi: 10.1016/j.csl.2019.101034
[29] Zhao, J., Zeng, D., Xiao, Y., Che, L., & Wang, M. (2020). User personality prediction based on topic preference and sentiment analysis using LSTM model. Pattern Recognition Letters, 138, 397-402. doi: 10.1016/j.patrec.2020.07.035
[30] Imrana, Y., Xiang, Y., Ali, L. and Abdul-Rauf, Z., 2021. A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems with Applications, 185, p.115524.
[31] Catelli, R., Casola, V., De Pietro, G., Fujita, H., & Esposito, M. (2021). Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification. Knowledge-Based Systems, 213, 106649. doi: 10.1016/j.knosys.2020.106649
[32] Liu, Y., Wang, L., Shi, T., & Li, J. (2021). Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM. Information Systems, 101865. doi: 10.1016/j.is.2021.101865
[33] Wang, Y., Zhang, M., Wu, R., Wang, H., Luo, Z., & Li, G. (2021). Speech neuromuscular decoding based on spectrogram images using conformal predictors with Bi-LSTM. Neurocomputing, 451, 25-34. doi: 10.1016/j.neucom.2021.03.025
[34] Gajendran, S., D, M., & Sugumaran, V. (2020). Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature. Journal Of Biomedical Informatics, 112, 103609. doi: 10.1016/j.jbi.2020.103609
[35] Nirmala, F. S., Indriati, & Rizal, S. P. (Eds.). (2018). Peringkasan Teks Otomatis Menggunakan Metode Maximum Marginal Relevance Pada Hasil Pencarian Sistem Temu Kembali Informasi Untuk Artikel Berbahasa Indonesia. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer , 2, 5494-5502.