Pemodelan topik pada dokumen paten terkait pupuk di Indonesia berbasis Latent Dirichlet Allocation
Abstract
Introduction. Fertilizer is one of the most important production factors in the world of agriculture. It is crucial to increase the capacity of technology related to fertilizers. Analysis of patent documents can be one way to analyze technological developments, especially fertilizers.
Data Collection Methods. The data used in this research are metadata, especially the title and abstract of a patent document in Indonesia. With the keyword "fertilizer," Patent metadata was processed in the 1945-2017 period.
Data Analysis. The LDA model can provide a reasonable interpretation regarding topic modeling based on text data.
Results and Discussion. The results find that degree of the patent title is better than the abstract of the patent. The LDA approach can adequately separate the topics of fertilizer patent technology so that it does not have multiple interpretations.
Conclusion. Based on the findings, there are nine essential topics in the development of fertilizer technology. There is a phenomenon of the lack of technology collaboration between IPC technology sections.
References
Adriani, M., Asian, J., Nazief, B., Williams, H. E., & Tahaghoghi, S. M. M. (2005). Stemming Indonesian : A Confix-Stripping Approach. Conferences in Research and Practice in Information Technology Series, 38(4), 307–314. https://doi.org/10.1145/1316457.1316459
Asian, J., Williams, H. E., & Tahaghoghi, S. M. M. (2005). Stemming Indonesian. Conferences in Research and Practice in Information Technology Series, 38(January), 307–314. https://doi.org/10.1145/1316457.1316459
Blei, D., Carin, L., & Dunson, D. (2012). Probabilistic topic models. Communications of the Acm, 27(6), 55–65. https://doi.org/10.1109/MSP.2010.938079
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. The Art and Science of Analyzing Software Data, 3, 139–159. https://doi.org/10.1016/B978-0-12-411519-4.00006-9
Campbell, J. C., Hindle, A., & Stroulia, E. (2015). Latent Dirichlet Allocation: Extracting topics from software engineering data. The Art and Science of Analyzing Software Data, 139–159. https://doi.org/10.1016/B978-0-12-411519-4.00006-9
Chuang, J., Manning, C. D., & Heer, J. (2012). Termite: Visualization techniques for assessing textual topic models. Proceedings of the Workshop on Advanced Visual Interfaces AVI, 74–77. https://doi.org/10.1145/2254556.2254572
FAO. (2016). Agricultural Cost of Production Statistics :Guidelines for Data Collection, Compilation and Dissemination (FAO (ed.)). Food and Agriculture Organization of the United Nations.
Hongshu, C., Guangquan, Z., Donghua, Z., & Jie, L. (2017). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technological Forecasting and Social Change, 119, 39–52. https://doi.org/10.1016/j.techfore.2017.03.009
Hu, J., Li, S., Hu, J., & Yang, G. (2018). A hierarchical feature extraction model for multi-label mechanical patent classification. Sustainability (Switzerland), 10(1), 219. https://doi.org/10.3390/su10010219
Kim, G., & Bae, J. (2017). A novel approach to forecast promising technology through patent analysis. Technological Forecasting and Social Change, 117, 228–237. https://doi.org/10.1016/j.techfore.2016.11.023
Liang, C., Weijiao, S., Guancan, Y., Jing, Z., & Xiaoping, L. (2016). A topic model integrating patent classification information for patent analysis. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 41(October), 123–126.
Mabey, B. (2015). Visualizing topic models. In Dato (Ed.), Data Science Summit and Dato Conference 2015. Dato, Inc.
Momeni, A., & Rost, K. (2016). Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling. Technological Forecasting and Social Change, 104, 16–29. https://doi.org/10.1016/j.techfore.2015.12.003
Presiden Republik Indonesia. (2016). Undang-Undang No 13 Tahun 2016:Paten (Issue 1).
Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 399–408. https://doi.org/10.1145/2684822.2685324
Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. https://doi.org/10.3115/v1/w14-3110
Suhyeon, K., Haecheong, P., & Junghye, L. (2020). Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis. Expert Systems with Applications, 152. https://doi.org/10.1016/j.eswa.2020.113401
Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94. https://doi.org/10.1016/j.is.2020.101582
WIPO. (2018). Guide to the International Patent Classification. WIPO (World Intellectual Property Organization). https://www.wipo.int/export/sites/www/classifications/ipc/en/guide/guide_ipc.pdf.
Yu, X., & Zhang, B. (2019). Obtaining advantages from technology revolution: A patent roadmap for competition analysis and strategy planning. Technological Forecasting and Social Change, 145(April), 273–283. https://doi.org/10.1016/j.techfore.2017.10.008
Yun, J., & Geum, Y. (2020). Automated classification of patents: A topic modeling approach. Computers and Industrial Engineering, 147. https://doi.org/10.1016/j.cie.2020.106636
Copyright (c) 2021 Berkala Ilmu Perpustakaan dan Informasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Berkala Ilmu Perpustakaan dan Informasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- Articles published in Berkala Ilmu Perpustakaan dan Informasi are licensed under a Creative Commons Attribution-ShareAlike 4.0 International license. You are free to copy, transform, or redistribute articles for any lawful purpose in any medium, provided you give appropriate credit to the original author(s) and Berkala Ilmu Perpustakaan dan Informasi, link to the license, indicate if changes were made, and redistribute any derivative work under the same license.
- Copyright on articles is retained by the respective author(s), without restrictions. A non-exclusive license is granted to Berkala Ilmu Perpustakaan dan Informasi to publish the article and identify itself as its original publisher, along with the commercial right to include the article in a hardcopy issue for sale to libraries and individuals.
- By publishing in Berkala Ilmu Perpustakaan dan Informasi, authors grant any third party the right to use their article to the extent provided by the Creative Commons Attribution-ShareAlike 4.0 International license.