Analisis Aspek Filsafat Sains pada Bidang Graph Mining dan Kemiripan Aturan Hukum
DOI:
https://doi.org/10.30989/teknomatika.v17i1.1301Abstract
Hukum di Indonesia berjumlah sangat banyak dan saling berhubungan atau merujuk antara satu sama lain. Salah satu fenomena hukum yang ada di Indonesia yaitu terdapat kemiripan aturan hukum sehingga menimbulkan masalah yaitu aturan hukum yang tumpang tindih dan tidak sinkron. Ketika aturan hukum yang baru akan dibentuk, sebaiknya tersedia tools yang dapat melakukan pemeriksaan terlebih dulu terhadap hukum lama sehingga tidak tumpang tindih dan sinkron. Knowledge graph (KG) digunakan pada penelitian ini untuk merepresentasikan hukum-hukum yang ada di Indonesia. Penelusuran KG tersebut akan dibantu dengan teknologi graph mining. Pada penelitian ini, dibahas pula aspek-aspek filsafat sains pada beberapa penelitian terdahulu yang berkaitan dengan topik penelitian ini.
References
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[3] VOA Indonesia, “UU di Indonesia: Terlalu Banyak, Tumpang Tindih dan Tidak Sinkron.” 2021.
[4] “Sekjen MK: Tumpang Tindih Aturan Sebabkan Ketidakpastian Hukum | Mahkamah Konstitusi Republik Indonesia.” .
[5] “Database Peraturan Perundang-undangan Indonesia -[PERATURAN.GO.ID].” .
[6] G. Riva, “The Potential and Limitations of Computational Law for Data Protection,” MIT Comput. Law Rep., Nov 2020.
[7] M. Abdurahman et al., “Lex2KG: Automatic Conversion of Legal Documents to Knowledge Graph,” 2021 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2021, hal. 1–5, 2021, doi: 10.1109/ICACSIS53237.2021.9631310.
[8] C. C. Aggarwal dan H. Wang, Managing and Mining Graph Data, vol. 40. New York: Springer Science+Business Media, 2010.
[9] M. Kuramochi dan G. Karypis, “An efficient algorithm for discovering frequent subgraphs,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 9, hal. 1038–1051, 2004, doi: 10.1109/TKDE.2004.33.
[10] S. U. Rehman, S. Asghar, dan S. J. Fong, “Optimized and frequent subgraphs: How are they related?,” IEEE Access, vol. 6, hal. 37237–37249, 2018, doi: 10.1109/ACCESS.2018.2846604.
[11] D. Song et al., “Building and Querying an Enterprise Knowledge Graph,” IEEE Trans. Serv. Comput., vol. 12, no. 3, hal. 356–369, 2019, doi: 10.1109/TSC.2017.2711600.
[12] L. Robb, T. Candy, dan F. Deane, “Regulatory overlap: A systematic quantitative literature review,” Regul. Gov., no. October, 2022, doi: 10.1111/rego.12504.
[13] C. Sansone dan G. Sperlí, “Legal Information Retrieval systems: State-of-the-art and open issues,” Inf. Syst., vol. 106, 2022, doi: 10.1016/j.is.2021.101967.
[14] P. Hacker, F. Naumann, T. Friedrich, S. Grundmann, A. Lehmann, dan H. Zech, “AI Compliance - Challenges of Bridging Data Science and Law,” J. Data Inf. Qual., vol. 14, no. 3, 2022, doi: 10.1145/3531532.
[15] A. Jazayeri dan C. C. Yang, “Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey,” IEEE Trans. Big Data, vol. 8, no. 6, hal. 1443–1462, 2022, doi: 10.1109/TBDATA.2021.3072001.
[16] S. Eddamiri, A. Benghabrit, dan E. Zemmouri, “RDF graph mining for cluster-based theme identification,” Int. J. Web Inf. Syst., vol. 16, no. 2, hal. 223–247, 2020, doi: 10.1108/IJWIS-10-2019-0048.
[17] D. R. Faisal, F. Darari, B. C. Lumban Tobing, O. Lee, B. C. L. Tobing, dan O. Lee, “Towards Building a Legal Virtual Assistant Based on Knowledge Graphs,” CEUR Workshop Proc., vol. 3257, hal. 73–78, 2022.
[18] T. S. Kuhn, The Structure of Scientific Revolutions. Chicago: The University of Chicago, 1962.
[19] U. Fayyad, G. Piatetsky-shapiro, dan P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, vol. 17, no. 3, hal. 37–54, 1996.
[20] I. Lakatos, The Methodology of Scientific Research Programmes. New York: Cambridge University Press, 1978.
[21] D. Mahayana, Filsafat Sains: Dari Newton, Einstein hingga Sains-Data. Bandung: ITB Press, 2022.
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Published
2024-05-17
How to Cite
Akhyar, A. (2024). Analisis Aspek Filsafat Sains pada Bidang Graph Mining dan Kemiripan Aturan Hukum. Teknomatika: Jurnal Informatika Dan Komputer, 17(1), 35–47. https://doi.org/10.30989/teknomatika.v17i1.1301
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