Analisis Teks untuk Official Statistics: Systematic Literature Review
DOI:
https://doi.org/10.26418/justin.v12i4.83426Keywords:
big data, pemodelan topik, klasifikasi teks, penambangan teksAbstract
Big data menghasilkan berbagai jenis data, termasuk data teks yang memiliki keunggulan dan berpotensi untuk meningkatkan kualitas official statistics. Belum tersedianya literatur yang membahas khusus tentang pemanfaatan analisis teks untuk official statistics mendorong dilakukannya penelitian dengan pendekatan Systematic Literature Review (SLR) guna mengidentifikasi tren penelitian, konsep dasar dan pemanfaatan, serta temuan dan tantangan analisis teks untuk official statistics. Tahapan SLR meliputi planning, data collection, analysis, dan discussion. Pada tahap planning, dirumuskan tiga pertanyaan penelitian sesuai tujuan penelitian. Data collection dilakukan dengan scraping untuk identifikasi tren literatur dan pencarian konvensional pada Google Scholar untuk mendapatkan publikasi relevan terkait pemanfaatan analisis teks. Tahap analysis memvisualisasikan tren penelitian menggunakan diagram batang, jaringan, dan word cloud, dilanjutkan dengan pembahasan pemanfaatan yang dibagi berdasarkan sektor ekonomi, sosial, dan lingkungan. Pada tahap discussion, dilakukan integrasi pembahasan untuk melihat temuan dan tantangan penerapan analisis teks untuk official statistics. Hasil penelitian menunjukkan bahwa secara keseluruhan, tren literatur yang sering dibahas pada kata kunci official statistics adalah klasifikasi teks untuk literatur berbahasa Indonesia, dan pemodelan topik untuk literatur berbahasa Inggris. Temuan yang diperoleh adalah analisis teks berpotensi memperkaya official statistics melalui prediksi ekonomi, analisis tren sosial, dan pemantauan lingkungan, analisis teks dapat digunakan untuk analisis tunggal maupun variabel pelengkap dalam penelitian. Tantangan utama terletak pada sifat teks yang tidak terstruktur dan fleksibilitasnya dalam berbagai penggunaan, sehingga diperlukan standar pemrosesan, jaminan kerahasiaan, regulasi yang memadai, serta kolaborasi nasional dan internasional agar analisis teks dapat terintegrasi secara efektif sesuai dengan prinsip-prinsip official statistics.
References
T. Petroc, “Data growth worldwide 2010-2025,†Statista. Accessed: Jul. 15, 2024. [Online]. Available: https://www.statista.com/statistics/871513/worldwide-data-created/
J. A. Shamsi, Big Data Systems: A 360-degree Approach. CRC Press, 2021.
W. J. Radermacher, Official statistics 4.0: Verified Facts for People in the 21st Century. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-31492-7.
M. Faris, “Artikel - Big Data BPS.†Accessed: Jul. 15, 2024. [Online]. Available: https://bigdata.bps.go.id/article/4
Z. Khan and T. Vorley, “Big data text analytics: an enabler of knowledge management,†JKM, vol. 21, no. 1, pp. 18–34, Feb. 2017, doi: 10.1108/JKM-06-2015-0238.
R. Hans, “Teknik Analisis Data Systematic Literature Review.†Accessed: Aug. 06, 2024. [Online]. Available: https://dqlab.id/teknik-analisis-data-systematic-literature-review
B. Drury and M. Roche, “A survey of the applications of text mining for agriculture,†Computers and Electronics in Agriculture, vol. 163, p. 104864, Aug. 2019, doi: 10.1016/j.compag.2019.104864.
S. Kumar, A. K. Kar, and P. V. Ilavarasan, “Applications of text mining in services management: A systematic literature review,†International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100008, Apr. 2021, doi: 10.1016/j.jjimei.2021.100008.
R. Milicich, T. Dickinson, G. Van Halderen, T. Lalor, and H. Niven, “Assessing compliance with the United Nations Fundamental Principles of Official statistics: A maturity model for continuous improvement1,†SJI, vol. 37, no. 2, pp. 525–537, Jun. 2021, doi: 10.3233/SJI-210805.
D. K. S. Kaswan and M. J. S. Dhatterwal, Big Data: An Introduction. Shashwat Publication, 2020.
S. Tam and F. Clarke, “Big Data, Official statistics and Some Initiatives by the Australian Bureau of Statistics,†Int Statistical Rev, vol. 83, no. 3, pp. 436–448, Dec. 2015, doi: 10.1111/insr.12105.
D. Sarkar, Text Analytics with Python: A Practitioner’s Guide to Natural Language Processing. Berkeley, CA: Apress, 2019. doi: 10.1007/978-1-4842-4354-1.
S. Khomsah and A. S. Aribowo, “Model Text-Preprocessing Komentar Youtube Dalam Bahasa Indonesia,†vol. 4, no. 4, 2020.
S. Pradha, M. N. Halgamuge, and N. Tran Quoc Vinh, “Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data,†in 2019 11th International Conference on Knowledge and Systems Engineering (KSE), Da Nang, Vietnam: IEEE, Oct. 2019, pp. 1–8. doi: 10.1109/KSE.2019.8919368.
A. Khosla, P. Chatterjee, I. Ali, and D. Joshi, Optimization Techniques in Engineering: Advances and Applications. John Wiley & Sons, 2023.
M. Irfani and S. Khomsah, “Analisis Sentimen Berbasis Aspek pada EDOM Pembelajaran Menggunakan Metode CNN dan Word2vec,†vol. 12, no. 3, 2024.
R. Adipradana, B. P. Nayoga, R. Suryadi, and D. Suhartono, “Hoax analyzer for Indonesian news using RNNs with fasttext and glove embeddings,†Bulletin of Electrical Engineering and Informatics, vol. 10, no. 4, Art. no. 4, Aug. 2021, doi: 10.11591/eei.v10i4.2956.
J. Rashid, S. M. A. Shah, and A. Irtaza, “An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering,†Mehran Univ. res. j. eng. technol., vol. 39, no. 1, pp. 213–222, Jan. 2020, doi: 10.22581/muet1982.2001.20.
T. Mantoro, M. A. Ayu, and R. T. Handayanto, “Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval,†in 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), Yogyakarta, Indonesia: IEEE, Sep. 2020, pp. 96–100. doi: 10.1109/IC2IE50715.2020.9274607.
K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text Classification Algorithms: A Survey,†Information, vol. 10, no. 4, p. 150, Apr. 2019, doi: 10.3390/info10040150.
N. Kumar, S. K. Yadav, and D. S. Yadav, “Similarity Measure Approaches Applied in Text Document Clustering for Information Retrieval,†in 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India: IEEE, Nov. 2020, pp. 88–92. doi: 10.1109/PDGC50313.2020.9315851.
L. Weston et al., “Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature,†J. Chem. Inf. Model., vol. 59, no. 9, pp. 3692–3702, Sep. 2019, doi: 10.1021/acs.jcim.9b00470.
D. P. Sholawatunnisa, L. H. Suadaa, U. Nugraha, and S. Pramana, “Indonesian GDP movement detection using online news classification,†Statistical Journal of the IAOS, vol. Preprint, no. Preprint, pp. 1–16, Jan. 2024, doi: 10.3233/SJI-230038.
S. Marchetti, C. Giusti, and M. Pratesi, “The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy,†AStA Wirtsch Sozialstat Arch, vol. 10, no. 2–3, pp. 79–93, Oct. 2016, doi: 10.1007/s11943-016-0190-4.
J. John, M. S. Varkey, and S. M, “Multi-class Text Classification and Publication of Crime Data from Online News Sources,†in 2021 8th International Conference on Smart Computing and Communications (ICSCC), Jul. 2021, pp. 64–63. doi: 10.1109/ICSCC51209.2021.9528127.
A. Sarker et al., “Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task,†Journal of the American Medical Informatics Association, vol. 25, no. 10, pp. 1274–1283, Oct. 2018, doi: 10.1093/jamia/ocy114.
T. Nugent, F. Petroni, N. Raman, L. Carstens, and J. L. Leidner, “A comparison of classification models for natural disaster and critical event detection from news,†in 2017 IEEE International Conference on Big Data (Big Data), Boston, MA: IEEE, Dec. 2017, pp. 3750–3759. doi: 10.1109/BigData.2017.8258374.
Y. Feng and M. Sester, Social media as a rainfall indicator. 2017.
E. da Costa, H. Tjandrasa, and S. Djanali, “Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging,†International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 3, Art. no. 3, Jun. 2018, doi: 10.11591/ijece.v8i3.pp1671-1683.
H. J. Kang, C. Kim, S. Kim, and C. Kim, “A Study on Environmental Trends and Sustainability in the Ocean Economy Using Topic Modeling: South Korean News Articles,†Processes, vol. 11, no. 8, p. 2253, Jul. 2023, doi: 10.3390/pr11082253.
E. Park, J. Park, and M. Hu, “Tourism demand forecasting with online news data mining,†Annals of Tourism Research, vol. 90, p. 103273, Sep. 2021, doi: 10.1016/j.annals.2021.103273.
Y. Jo, L. Lee, and S. Palaskar, “Combining LSTM and Latent Topic Modeling for Mortality Prediction,†Sep. 08, 2017, arXiv: arXiv:1709.02842. Accessed: Jul. 14, 2024. [Online]. Available: http://arxiv.org/abs/1709.02842
R. Vijayan, “Teaching and Learning during the COVID-19 Pandemic: A Topic Modeling Study,†Education Sciences, vol. 11, no. 7, p. 347, Jul. 2021, doi: 10.3390/educsci11070347.
E. L. Jenkins, D. Lukose, L. Brennan, A. Molenaar, and T. A. McCaffrey, “Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data,†Sustainability, vol. 15, no. 18, p. 13788, Sep. 2023, doi: 10.3390/su151813788.
M. Hagras, G. Hassan, and N. Farag, “Towards Natural Disasters Detection from Twitter Using Topic Modelling,†in 2017 European Conference on Electrical Engineering and Computer Science (EECS), Bern: IEEE, Nov. 2017, pp. 272–279. doi: 10.1109/EECS.2017.57.
S. Mohan, S. Mullapudi, S. Sammeta, P. Vijayvergia, and D. C. Anastasiu, “Stock Price Prediction Using News Sentiment Analysis,†in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA: IEEE, Apr. 2019, pp. 205–208. doi: 10.1109/BigDataService.2019.00035.
L.-T. Zhao, G.-R. Zeng, W.-J. Wang, and Z.-G. Zhang, “Forecasting Oil Price Using Web-based Sentiment Analysis,†Energies, vol. 12, no. 22, p. 4291, Nov. 2019, doi: 10.3390/en12224291.
C. R. Nirmala, G. M. Roopa, and K. R. Naveen Kumar, “Twitter data analysis for unemployment crisis,†in 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere: IEEE, Oct. 2015, pp. 420–423. doi: 10.1109/ICATCCT.2015.7456920.
D. Wang, A. Al-Rubaie, B. Hirsch, and G. C. Pole, “National happiness index monitoring using Twitter for bilanguages,†Soc. Netw. Anal. Min., vol. 11, no. 1, p. 24, Feb. 2021, doi: 10.1007/s13278-021-00728-0.
H. Li, Z. Jadidi, J. Chen, and J. Jo, “The Use of Machine Learning for Correlation Analysis of Sentiment and Weather Data,†in Robot Intelligence Technology and Applications 5, vol. 751, J.-H. Kim, H. Myung, J. Kim, W. Xu, E. T. Matson, J.-W. Jung, and H.-L. Choi, Eds., in Advances in Intelligent Systems and Computing, vol. 751. , Cham: Springer International Publishing, 2019, pp. 291–298. doi: 10.1007/978-3-319-78452-6_25.
R. K. Mishra, H. Raj, S. Urolagin, J. A. A. Jothi, and N. Nawaz, “Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments,†Applied Sciences, vol. 12, no. 16, p. 8105, Aug. 2022, doi: 10.3390/app12168105.
X. Cheng et al., “Symptom Clustering Patterns and Population Characteristics of COVID-19 Based on Text Clustering Method,†Front. Public Health, vol. 10, p. 795734, Feb. 2022, doi: 10.3389/fpubh.2022.795734.
Q. Bsoul, J. Salim, and L. Q. Zakaria, “An Intelligent Document Clustering Approach to Detect Crime Patterns,†Procedia Technology, vol. 11, pp. 1181–1187, 2013, doi: 10.1016/j.protcy.2013.12.311.
S. Bhulai, DATA ANALYTICS 2016 the Fifth International Conference on Data Analytics: October 9-13, 2016, Venice, Italy. Wilmington, DE, USA: IARIA, 2016.
L. Huang, P. Shi, H. Zhu, and T. Chen, “Early detection of emergency events from social media: a new text clustering approach,†Nat Hazards, vol. 111, no. 1, pp. 851–875, Mar. 2022, doi: 10.1007/s11069-021-05081-1.
E. Van Der Zee and D. Bertocchi, “Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews,†Inf Technol Tourism, vol. 20, no. 1–4, pp. 153–180, Dec. 2018, doi: 10.1007/s40558-018-0128-5.
J. P. Trinidad, M. Warner, B. A. Bastian, A. M. Miniño, and H. Hedegaard, “Using literal text from the death certificate to enhance mortality statistics : characterizing drug involvement in deaths,†2016, Accessed: Jul. 14, 2024. [Online]. Available: https://stacks.cdc.gov/view/cdc/43279
European Commission. Statistical Office of the European Union., An introduction to large language models and their relevance for statistical offices: 2024 edition. LU: Publications Office, 2024. Accessed: Jul. 15, 2024. [Online]. Available: https://data.europa.eu/doi/10.2785/716217
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