Sentiment Analysis of Cooking Oil Prices in Indonesia Using the Long Short-Term Memory Method
Keywords:
Keywords, Sentiment Analysis, Text Mining, Classification, LSTMAbstract
The policy that was intensively discussed from early January to March 2022 was related to the setting and lifting of the Highest Retail Price for cooking oil, which was proven by the busy news on television, print media and social media. Many responses or speculations arise as a result of this policy. The importance of research on public speculation or sentiment analysis is to create a system model and find out how the public responds to government policy after the Highest Retail Price for cooking oil is determined and revoked, which ranges from February 4 to March 31 2022, as a benchmark and material. government considerations in making policy. The data that was collected in the period after the Maximum Retail Price (MRP) was set amounted to 904 datasets, and after the repeal of the MRP, it amounted to 874. Research can function as a basis for completing important information to support public policy decisions. The data is trained to obtain an optimal model and can predict sentiment with the Long Short Term Memory (LSTM) model. To get the best model, random parameter testing was carried out using 80% of the training data and 20% of the validation dataset. The test results in fairly good accuracy with the softmax activation function, with an accuracy of 82.34%.
References
D. T. Hermanto, A. Setyanto, and E. T. Luthfi, "Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online," Creat. Inf. Technol. J., vol. 8, no. 1, p. 64, 2021, doi: 10.24076/citec.2021v8i1.264.
N. DePaula, E. Dincelli, and T. M. Harrison, "Toward a typology of government social media communication: Democratic goals, symbolic acts and self-presentation," Gov. Inf. Q., vol. 35, no. 1, pp. 98–108, 2018, doi: https://doi.org/10.1016/j.giq.2017.10.003.
I. Isharyanto, "Penetapan Harga Eceran Tertinggi Komoditas Pangan sebagai Hak Konstitusional dalam Perspektif Negara Kesejahteraan," J. Konstitusi, vol. 15, no. 3, p. 525, 2018, doi: 10.31078/jk1534.
K. R. Chowdhary, "Natural Language Processing BT - Fundamentals of Artificial Intelligence," K. R. Chowdhary, Ed., New Delhi: Springer India, 2020, pp. 603–649. doi: 10.1007/978-81-322-3972-7_19.
E. D. Sri Mulyani, D. Rohpandi, and F. A. Rahman, "Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia," in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), 2019, pp. 89–93. doi: 10.1109/ICORIS.2019.8874896.
I. H. Teuku Mufizar, N Nelis Febriani SM, Hendri Julian Pramana, "Analisis Sentimen Pengguna Twitter Terhadap Vaksin Sinovac (Covid-19) Dengan Menggunakan Metode Naí¯ve Bayes," Comput. Sci. Res. Its Dev. J., vol. 15, no. 1, pp. 12–21, 2023.
F. R. Utama, D. S. A. Maylawati, U. Syaripudin, and ..., "Sentiment Analysis Regarding the Name of "˜Nusantara' in Indonesia's New Capital City Using Convolutional Neural Network," 2023 IEEE 9th ..., 2023, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10425579/
M. Z. Rahman, Y. A. Sari, and N. Yudistira, "Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM)," J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5120–5127, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
L. Kurniasari and A. Setyanto, "Sentiment Analysis using Recurrent Neural Network," J. Phys. Conf. Ser., vol. 1471, no. 1, pp. 1–4, 2020, doi: 10.1088/1742-6596/1471/1/012018.
F. Huang, X. Li, C. Yuan, S. Zhang, J. Zhang, and S. Qiao, "Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis," IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 9, pp. 4332–4345, 2022, doi: 10.1109/TNNLS.2021.3056664.
Z. Jin, Y. Yang, and Y. Liu, "Stock closing price prediction based on sentiment analysis and LSTM," Neural Comput. Appl., vol. 32, no. 13, pp. 9713–9729, 2020, doi: 10.1007/s00521-019-04504-2.
G. S. . Murthy, S. R. Allu, B. Andhavarapu, M. Bgadi, and M. Belusonti, "Text based Sentiment Analysis using Long Short Term Memory (LSTM)," Int. J. Eng. Res. Technol., vol. 9, no. 05, pp. 299–303, 2020.
Y. Zhang et al., "Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis," Neural Networks, vol. 133, pp. 40–56, 2021, doi: https://doi.org/10.1016/j.neunet.2020.10.001.
R. K. Behera, M. Jena, S. K. Rath, and S. Misra, "Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data," Inf. Process. Manag., vol. 58, no. 1, p. 102435, 2021, doi: https://doi.org/10.1016/j.ipm.2020.102435.
M. S. Divate, "Sentiment analysis of Marathi news using LSTM," Int. J. Inf. Technol., vol. 13, no. 5, pp. 2069–2074, 2021, doi: 10.1007/s41870-021-00702-1.
N. Suhermi, S. Suhartono, I. M. G. M. Dana, and D. D. Prastyo, "Pemilihan Arsitektur Terbaik pada Model Deep Learning Melalui Pendekatan Desain Eksperimen untuk Peramalan Deret Waktu Nonlinier," Stat. J. Theor. Stat. Its Appl., vol. 18, no. 2, pp. 153–159, 2019, doi: 10.29313/jstat.v18i2.4545.
A. Martins and R. Astudillo, "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification," in Proceedings of The 33rd International Conference on Machine Learning, M. F. Balcan and K. Q. Weinberger, Eds., in Proceedings of Machine Learning Research, vol. 48. New York, New York, USA: PMLR, 2016, pp. 1614–1623. [Online]. Available: https://proceedings.mlr.press/v48/martins16.html
F. A. Nugraha, N. H. Harani, R. Habibi, and R. N. S. Fatonah, "Sentiment Analysis on Social Distancing and Physical Distancing on Twitter Social Media using Recurrent Neural Network (RNN) Algorithm," J. Online Inform., vol. 5, no. 2, p. 195, 2020, doi: 10.15575/join.v5i2.632.
J. Eka Sembodo, E. Budi Setiawan, and Z. Abdurahman Baizal, "Data Crawling Otomatis pada Twitter," no. October 2018, pp. 11–16, 2016, doi: 10.21108/indosc.2016.111.
R. Wijayanti and A. Arisal, "Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis," ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 20, no. 1, Mar. 2021, doi: 10.1145/3425632.
Q. F. Nabila and A. Fitrisia, "Perkembangan dan Potensi Perekonomian Usaha Tanaman Hias di Kelurahan Lubuk Minturun Koto Tangah Kota Padang (1991-2020)," J. Kronologi, 2023, [Online]. Available: http://kronologi.ppj.unp.ac.id/index.php/jk/article/view/544
"Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat," Dep. Informatics, Univ. Islam Negeri Syarif Hidayatullah, vol. Vol 12, No, 2019.
J. M. S. Waworundeng, G. A. Sandag, R. A. Sahulata, and G. D. Rellely, "Sentiment Analysis of Online Lectures Tweets using Naí¯ve Bayes Classifier," CogITo Smart J., vol. 8, no. 2, pp. 371–384, 2022, doi: 10.31154/cogito.v8i2.414.371-384.
S. Kumar and U. B. Roy, "2 - A technique of data collection: web scraping with python," T. Goswami and G. R. B. T.-S. M. in M. L. Sinha, Eds., Academic Press, 2023, pp. 23–36. doi: https://doi.org/10.1016/B978-0-323-91776-6.00011-7.
T. Zhang, D. P. Chandrasekaran, F. Thung, and D. Lo, "Benchmarking library recognition in tweets," in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, in ICPC '22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 343–353. doi: 10.1145/3524610.3527916.
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2025 Elinvo (Electronics, Informatics, and Vocational Education)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The article published in ELINVO became ELINVO's right in publication.
This work by ELINVO is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.