Transformasi Portal Data Pemerintah di Indonesia dengan Large Language Model dan Retrieval-Augmented Generation: Tinjauan Pustaka Sistematis
DOI:
https://doi.org/10.52436/1.jpti.1175Kata Kunci:
E-Government, Kecerdasan Buatan, Large Language Model, Portal Data Pemerintah, Retrieval-Augmented GenerationAbstrak
Integrasi kecerdasan buatan (Artificial Intelligence/AI) seperti Large Language Model (LLM) dan Retrieval-Augmented Generation (RAG) berpotensi mentransformasi portal data pemerintah, namun implementasinya terhambat oleh kurangnya tinjauan sistematis dan kerangka evaluasi yang spesifik. Penelitian ini bertujuan untuk mengidentifikasi, mengevaluasi, dan mensintesis literatur terkini mengenai metodologi, keberhasilan, dan tantangan integrasi teknologi tersebut melalui tinjauan pustaka sistematis. Metode ini diterapkan dengan pencarian terstruktur pada basis data Google Scholar, Scopus, dan IEEE Xplore, diikuti proses penyaringan bertahap. Hasil tinjauan menunjukkan bahwa teknologi AI terbukti efektif meningkatkan komunikasi pemerintah-warga, efisiensi layanan, dan akurasi pengambilan data, di mana penyesuaian model menjadi faktor penting. Namun, implementasinya masih menghadapi tantangan signifikan terkait tata kelola, kualitas data, dan masalah etis. Hasil penelitian ini menekankan pentingnya pengembangan kerangka kerja tata kelola yang komprehensif untuk memastikan penerapan AI yang akuntabel dan selaras dengan kepentingan publik.
Unduhan
Referensi
A. Androutsopoulou, N. Karacapilidis, E. Loukis, dan Y. Charalabidis, “Transforming the communication between citizens and government through AI-guided chatbots,” Gov. Inf. Q., vol. 36, no. 2, hlm. 358–367, Apr 2019, doi: 10.1016/j.giq.2018.10.001.
G. Papageorgiou, V. Sarlis, M. Maragoudakis, dan C. Tjortjis, “Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models,” Appl. Sci., vol. 14, no. 18, hlm. 8259, Sep 2024, doi: 10.3390/app14188259.
J. Han, J. Lu, Y. Xu, J. You, dan B. Wu, “Intelligent Practices of Large Language Models in Digital Government Services,” IEEE Access, vol. 12, hlm. 8633–8640, 2024, doi: 10.1109/ACCESS.2024.3349969.
N. Moldabay, "Transformation from electronic government to smart government," Tesis Magister, Dept. Manajemen, Univ. degli Studi di Teramo, Teramo, Italia, Okt. 2022.
L. Yun, S. Yun, dan H. Xue, “Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models,” PLOS ONE, vol. 19, no. 12, hlm. e0311410, Des 2024, doi: 10.1371/journal.pone.0311410.
H. Chafetz, S. Saxena, dan S. G. Verhulst, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI,” 2024, arXiv. doi: 10.48550/ARXIV.2405.04333.
M. Kuziemski dan G. Misuraca, “AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings,” Telecommun. Policy, vol. 44, no. 6, hlm. 101976, Jul 2020, doi: 10.1016/j.telpol.2020.101976.
K. Fang dan K. Xu, “Automating Government Response to Citizens’ Questions: A Large Language Model-Based Question-Answering Guidance Generation System,” dalam 2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS), Chengdu, China: IEEE, Nov 2023, hlm. 386–389. doi: 10.1109/DSInS60115.2023.10455136.
O. Marjanovic dan D. Cecez-Kecmanovic, “Open government data platforms – A complex adaptive sociomaterial systems perspective,” Inf. Organ., vol. 30, no. 4, hlm. 100323, Des 2020, doi: 10.1016/j.infoandorg.2020.100323.
K. Hardy dan A. Maurushat, “Opening up government data for Big Data analysis and public benefit,” Comput. Law Secur. Rev., vol. 33, no. 1, hlm. 30–37, Feb 2017, doi: 10.1016/j.clsr.2016.11.003.
S. Liu, L. Zhang, W. Liu, J. Zhang, D. Gao, dan X. Jia, “The Evaluation Framework and Benchmark for Large Language Models in the Government Affairs Domain,” ACM Trans. Intell. Syst. Technol., hlm. 3716854, Feb 2025, doi: 10.1145/3716854.
M. A. Alqudah, “Towards the governance of government data using artificial intelligence,” 23 Desember 2021, Social Science Research Network, Rochester, NY: 3992303. doi: 10.2139/ssrn.3992303.
E. Tan, “Designing an AI compatible open government data ecosystem for public governance,” Inf. Polity, vol. 28, no. 4, hlm. 541–557, Nov 2023, doi: 10.3233/IP-220020.
X. Li dan J. Wang, “Should government chatbots behave like civil servants? The effect of chatbot identity characteristics on citizen experience,” Gov. Inf. Q., vol. 41, no. 3, hlm. 101957, Sep 2024, doi: 10.1016/j.giq.2024.101957.
K. Fang, Y. Chai, dan C. K. Xu, “Detecting Digital Government Answer Quality: an Integrated Method Based on LargeLanguage Models and Machine Learning Models: Detecting Digital Government Answer Quality,” dalam Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning, Shanghai China: ACM, Mar 2024, hlm. 7–12. doi: 10.1145/3654823.3654825.
P. Mikalef dkk., “Enabling AI capabilities in government agencies: A study of determinants for European municipalities,” Gov. Inf. Q., vol. 39, no. 4, hlm. 101596, Okt 2022, doi: 10.1016/j.giq.2021.101596.
G. V. Pereira, M. A. Macadar, E. M. Luciano, dan M. G. Testa, “Delivering public value through open government data initiatives in a Smart City context,” Inf. Syst. Front., vol. 19, no. 2, hlm. 213–229, Apr 2017, doi: 10.1007/s10796-016-9673-7.
S. S. Dawes, L. Vidiasova, dan O. Parkhimovich, “Planning and designing open government data programs: An ecosystem approach,” Gov. Inf. Q., vol. 33, no. 1, hlm. 15–27, Jan 2016, doi: 10.1016/j.giq.2016.01.003.
I. Pencheva, M. Esteve, dan S. J. Mikhaylov, “Big Data and AI – A transformational shift for government: So, what next for research?,” Public Policy Adm., vol. 35, no. 1, hlm. 24–44, Jan 2020, doi: 10.1177/0952076718780537.
M. Mamalis, E. Kalampokis, F. Fitsilis, G. Theodorakopoulos, dan K. Tarabanis, “A Large Language Model based legal assistant for governance applications,” 2 Mei 2024, Open Science Framework. doi: 10.31219/osf.io/94fmq.
M. E. Mamalis, E. Kalampokis, A. Karamanou, P. Brimos, dan K. Tarabanis, “Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in statistics.gov.scot,” dalam Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics, dalam PCI ’23. New York, NY, USA: Association for Computing Machinery, Feb 2024, hlm. 53–59. doi: 10.1145/3635059.3635068.
O. Alqaryouti, N. Siyam, A. Abdel Monem, dan K. Shaalan, “Aspect-based sentiment analysis using smart government review data,” Appl. Comput. Inform., vol. 20, no. 1/2, hlm. 142–161, Jan 2024, doi: 10.1016/j.aci.2019.11.003.
S. Baack dkk., “Towards Best Practices for Open Datasets for LLM Training,” 14 Januari 2025, arXiv: arXiv:2501.08365. doi: 10.48550/arXiv.2501.08365.
D. Canning dan L. Jaillant, “AI to review government records: new work to unlock historically significant digital records,” AI Soc., Feb 2025, doi: 10.1007/s00146-025-02221-0.
C. Cao, J. Zhuang, dan Q. He, “LLM-Assisted Modeling and Simulations for Public Sector Decision-Making: Bridging Climate Data and Policy Insights,” dipresentasikan pada AAAI-2024 Workshop on Public Sector LLMs: Algorithmic and Sociotechnical Design, Feb 2024.
P. Dunleavy dan H. Margetts, “Data science, artificial intelligence and the third wave of digital era governance,” Public Policy Adm., vol. 40, no. 2, hlm. 185–214, Apr 2025, doi: 10.1177/09520767231198737.
D. Fares, “The Role of Large Language Models (LLMs) Driven Chatbots in Shaping the Future of Government Services and Communication with Citizens in UAE,” Theses, Des 2023, [Daring]. Tersedia pada: https://repository.rit.edu/theses/11694
U. Gasser dan V. A. F. Almeida, “A Layered Model for AI Governance,” IEEE Internet Comput., vol. 21, no. 6, hlm. 58–62, Nov 2017, doi: 10.1109/MIC.2017.4180835.
J. V. Lucke dan S. Frank, “A few Thoughts on the Use of ChatGPT, GPT 3.5, GPT-4 and LLMs in Parliaments: Reflecting on the results of experimenting with LLMs in the parliamentarian context,” Digit. Gov. Res. Pract., hlm. 3665333, Mei 2024, doi: 10.1145/3665333.
S. Mehrban dkk., “Towards Secure FinTech: A Survey, Taxonomy, and Open Research Challenges,” Ieee Access, vol. 8, hlm. 23391–23406, 2020, doi: 10.1109/access.2020.2970430.
E. S. de Paiva dkk., “Continued pre-training of LLMs for Portuguese and Government domain: A proposal for product identification in textual purchase descriptions,” dipresentasikan pada AAAI-2024 Workshop on Public Sector LLMs: Algorithmic and Sociotechnical Design, Feb 2024.
I. Safarov, A. Meijer, dan S. Grimmelikhuijsen, “Utilization of open government data: A systematic literature review of types, conditions, effects and users,” Inf. Polity, vol. 22, no. 1, hlm. 1–24, Feb 2017, doi: 10.3233/IP-160012.
Y. Wang dkk., “Large language models and their application in government affairs,” J. Tsinghua Univ. Sci. Technol., vol. 64, no. 4, hlm. 649–658, Apr 2024, doi: 10.16511/j.cnki.qhdxxb.2023.26.042.
T. Wiseman, J. Luckstead, dan A. Durand?Morat, “Asymmetric Exchange Rate Pass-Through in Southeast Asian Rice Trade,” J. Agric. Appl. Econ., vol. 53, no. 3, hlm. 341–374, 2021, doi: 10.1017/aae.2021.7.
J. Yae, “A Staged Framework for LLM-powered Information Extraction in Government Contracts,” Theses Diss., Mar 2024, [Daring]. Tersedia pada: https://scholar.afit.edu/etd/7734
W. Zhang, M. Wang, dan Y. Zhu, “Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics,” Int. J. Inf. Manag., vol. 50, hlm. 498–514, Feb 2020, doi: 10.1016/j.ijinfomgt.2019.04.001.
A. Zuiderwijk, Y.-C. Chen, dan F. Salem, “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda,” Gov. Inf. Q., vol. 38, no. 3, hlm. 101577, Jul 2021, doi: 10.1016/j.giq.2021.101577.
