Analysis Large Language Model (LLM) for Digital Transformation in a Ministry

Abstract
Digital transformation in government requires the use of cutting-edge technology to improve efficiency and accuracy in decision-making. Ministries are strategic institutions that manage large-scale data and information, particularly related to regional government administration and population affairs. This article examines the application of Large Language Models (LLM) as an artificial intelligence (AI)-based solution to assist document processing, information extraction, and policy analysis. The study presents a relevant legal framework, LLM model recommendations, a schematic diagram of LLM implementation from end-to-end, and Python code examples for technical simulations. This approach is expected to strengthen the Ministry's digital system in accordance with the guidelines of the Electronic-Based Government System (SPBE) and One Data Indonesia.
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How to Cite

Andre Pratama Adiwijaya (2025). Analysis Large Language Model (LLM) for Digital Transformation in a Ministry. International Journal Science and Technology (IJST), 4(2). https://doi.org/10.56127/ijst.v4i2.2194

Andre Pratama Adiwijaya, "Analysis Large Language Model (LLM) for Digital Transformation in a Ministry," International Journal Science and Technology (IJST), vol. 4, no. 2, 2025.

Andre Pratama Adiwijaya. "Analysis Large Language Model (LLM) for Digital Transformation in a Ministry." International Journal Science and Technology (IJST), vol. 4, no. 2, 2025.

Andre Pratama Adiwijaya. "Analysis Large Language Model (LLM) for Digital Transformation in a Ministry." International Journal Science and Technology (IJST) 4, no. 2 (2025).

Andre Pratama Adiwijaya (2025) 'Analysis Large Language Model (LLM) for Digital Transformation in a Ministry', International Journal Science and Technology (IJST), 4(2). doi: 10.56127/ijst.v4i2.2194.

Andre Pratama Adiwijaya. Analysis Large Language Model (LLM) for Digital Transformation in a Ministry. International Journal Science and Technology (IJST). 2025;4(2).

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