An AI-Driven Policy Intelligence Framework for Transforming National Data into Evidence-Based Public Policy
DOI:
https://doi.org/10.23917/saintek.v2i2.16988Keywords:
artificial intelligence, public policy, decision support system, data-driven governance, policy intelligenceAbstract
The increasing complexity of national development requires public policies that are adaptive, data-driven, and evidence-based. However, many governments, particularly in developing countries, still face significant challenges in utilizing national data effectively due to data fragmentation, limited analytical capabilities of information systems, and the underutilization of Artificial Intelligence (AI). These limitations hinder the formulation of accurate and proactive public policies. This study aims to propose a conceptual framework that integrates national data, Information Systems, and AI to support intelligent policymaking. This research adopts a Design Science Research (DSR) approach to develop an artifact in the form of the National AI-Driven Policy Intelligence Framework (NAPIF). The framework is designed using a layered architecture consisting of data, processing, and output layers, supported by AI capabilities such as pattern recognition, predictive analytics, and policy recommendation generation. The proposed model transforms fragmented data into actionable insights through an integrated system that supports decision-making processes. The results indicate that the proposed framework enhances data integration, improves analytical capabilities, and enables predictive and adaptive policymaking. Compared to conventional systems, the framework provides more comprehensive decision support and supports continuous policy improvement through a feedback-driven mechanism. The study contributes theoretically by integrating the domains of Information Systems, AI, and public policy into a unified framework, and practically by offering a strategic approach for governments to implement data-driven governance aligned with long-term development goals. This study is limited by its conceptual nature; therefore, future research is recommended to validate the framework through empirical implementation and real-world case studies.
Downloads
References
[1] M. J. Ahn and Y.-C. Chen, “Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government,” Gov. Inf. Q., vol. 39, no. 2, p. 101664, Apr. 2022, doi: 10.1016/j.giq.2021.101664.
[2] Y. E. Rachmad, The Future of Sovereign Wealth Management: Danantara’s Role in Indonesia Emas 2045. 2025. doi: 10.17605/osf.io/bd7a2.
[3] A. Kerasidou and C. Kerasidou, “Data-driven research and healthcare: public trust, data governance and the NHS,” BMC Med. Ethics, vol. 24, no. 1, p. 51, Jul. 2023, doi: 10.1186/s12910-023-00922-z.
[4] V. S. de Araujo, B. A. Zullo, and M. Torres, “Big data, algoritmos e inteligência artificial na administração pública: reflexões para a sua utilização em um ambiente democrático,” A&C - Revista de Direito Administrativo & Constitucional, vol. 20, no. 80, p. 241, Sep. 2020, doi: 10.21056/aec.v20i80.1219.
[5] W. Wang et al., “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Oct. 2021, pp. 548–558. doi: 10.1109/ICCV48922.2021.00061.
[6] Q. An, Z. Tao, X. Xu, M. El Mansori, and M. Chen, “A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network,” Measurement, vol. 154, p. 107461, Mar. 2020, doi: 10.1016/j.measurement.2019.107461.
[7] F. Santoso and A. Finn, “A Data-Driven Cyber–Physical System Using Deep-Learning Convolutional Neural Networks: Study on False-Data Injection Attacks in an Unmanned Ground Vehicle Under Fault-Tolerant Conditions,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 1, pp. 346–356, Jan. 2023, doi: 10.1109/TSMC.2022.3170071.
[8] W. Shamdi, D. Lai, A. A. Aziz, and M. Anshari, “Artificial intelligence development in Islamic System of Governance: a literature review,” Contemporary Islam, vol. 16, no. 2–3, pp. 321–334, Oct. 2022, doi: 10.1007/s11562-022-00504-7.
[9] M. Françoise et al., “Evidence based policy making during times of uncertainty through the lens of future policy makers: four recommendations to harmonise and guide health policy making in the future,” Archives of Public Health, vol. 80, no. 1, p. 140, Dec. 2022, doi: 10.1186/s13690-022-00898-z.
[10] S. L. B. Campos and J. M. Figueiredo, “Public services recommendation system: an alternative to customize the digital government transformation,” in Proceedings of the 24th Annual International Conference on Digital Government Research, New York, NY, USA: ACM, Jul. 2023, pp. 370–379. doi: 10.1145/3598469.3598511.
[11] H.-H. Chen, H. H.-S. Lu, W.-H. Weng, and Y.-H. Lin, “Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study,” J. Med. Internet Res., vol. 25, p. e48834, Dec. 2023, doi: 10.2196/48834.
[12] L. L. Evinita, J. E. M. Tangkau, P. J. Pesak, and S. Cahyono, “Policy Framework to Improve MSME Competitiveness and Financial Performance with Indonesia’s Asta Cita Vision Goals,” Journal of Risk and Financial Management, vol. 18, no. 12, p. 692, Dec. 2025, doi: 10.3390/jrfm18120692.
[13] R. M. Waterhouse et al., “Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR,” F1000Res., vol. 10, p. 1238, Dec. 2021, doi: 10.12688/f1000research.73825.1.
[14] F. Abdelfattah, M. Salah, K. Dahleez, R. Darwazeh, and H. Al Halbusi, “Public policy and sustainability: How green core competence, government trust, and policy satisfaction influence green R&D investments in the private sector,” Sustainable Futures, vol. 9, p. 100461, Jun. 2025, doi: 10.1016/j.sftr.2025.100461.
[15] Z. Li and S. Gietel-Basten, “Public-Private Partnerships in Home- and Community-Based Services for Older People in China: The Case of Guangzhou,” Social Policy and Society, pp. 1–16, Dec. 2025, doi: 10.1017/S1474746425101255.
[16] S. Iskandarova and M. F. Sloan, “Exploring Nonprofit and Government Agency AI Policies and Regulations: Systems Leadership,” in 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), IEEE, Dec. 2023, pp. 197–203. doi: 10.1109/GCAIoT61060.2023.10385109.
[17] M. Guia, R. Silva, and J. Bernardino, “Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis,” in Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, SCITEPRESS - Science and Technology Publications, 2019, pp. 525–531. doi: 10.5220/0008364105250531.
[18] S. E. Bibri, J. Huang, and J. Krogstie, “Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance,” Sustain. Cities Soc., vol. 108, p. 105516, Aug. 2024, doi: 10.1016/j.scs.2024.105516.
[19] Y. Cho, D. Kim, and J. Kim, “Data-driven demand response aggregation for public EV charging stations: Overcoming decoupled governance challenges,” Appl. Energy, vol. 402, p. 126986, Jan. 2026, doi: 10.1016/j.apenergy.2025.126986.
[20] F. Bertacchini, C. Scuro, P. Pantano, and E. Bilotta, “A Project Based Learning Approach for Improving Students’ Computational Thinking Skills,” Front. Robot. AI, vol. 9, Mar. 2022, doi: 10.3389/frobt.2022.720448.
[21] M. A. Bujang, E. D. Omar, D. H. P. Foo, and Y. K. Hon, “Sample size determination for conducting a pilot study to assess reliability of a questionnaire,” Restor. Dent. Endod., vol. 49, no. 1, 2024, doi: 10.5395/rde.2024.49.e3.
[22] K. Wang et al., “Research on AI-Driven Teaching Reform of Microelectronics Science and Engineering in Applied Undergraduate Institutions from the Perspective of Industry-Education Integration -- A Case Study on the Curriculum Construction of Design and Implementation of Artificial Intelligence Systems,” in Proceedings of the 2nd International Conference on Intelligent Education and Computer Technology, New York, NY, USA: ACM, Jun. 2025, pp. 891–900. doi: 10.1145/3764206.3764346.
[23] L. Lin, W.-K. Chiou, P.-C. Shen, S.-E. Hsu, H. Chen, and C. Liu, “Meta-Analysis of the Application of Artificial Intelligence and Spiritual Science in Guiding Learner Imagery Transformation: An Action Research on Quantum Resonance Design Course in University General Education Curriculum,” 2025, pp. 107–119. doi: 10.1007/978-3-031-76815-6_9.
[24] P. Fernández-López et al., “Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights,” Coatings, vol. 14, no. 8, p. 979, Aug. 2024, doi: 10.3390/coatings14080979.
[25] M. A. Hoffmann and R. Lasch, “Tackling Industrial Downtimes with Artificial Intelligence in Data-Driven Maintenance,” ACM Comput. Surv., vol. 56, no. 4, pp. 1–33, Apr. 2024, doi: 10.1145/3623378.
[26] G. Liu et al., “Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism,” Advanced Engineering Informatics, vol. 62, p. 102883, Oct. 2024, doi: 10.1016/j.aei.2024.102883.
[27] D. D. Ohnmeiss, D. C. Stastny, Z. Buser, and L. A. Ferrara, “Fusion cage design, materials, and coatings: Science versus hype,” North American Spine Society Journal (NASSJ), vol. 24, p. 100814, Dec. 2025, doi: 10.1016/j.xnsj.2025.100814.
[28] A. Amali, D. Maulana, E. Widodo, A. Firmansyah, and M. Danny, “Sentiment Analysis of Bekasi Floods Using SVM and Naive Bayes with Advanced Feature Selection,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 362–371, Jul. 2024, doi: 10.47709/brilliance.v4i1.4268.
[29] Y.-M. Wang and Y.-X. Li, “Adaptive security control of time-varying constraints nonlinear cyber-physical systems with false data injection attacks,” Journal of Control and Decision, vol. 11, no. 1, pp. 50–59, Jan. 2024, doi: 10.1080/23307706.2022.2136274.
[30] D. Fuadi, H. Harsono, M. F. J. Syah, A. Susilo, S. Suhaili, and B. Wahyono, “Self-Governance: Internationalization Management of Distinctive Higher Education Towards The World Class University,” Indonesian Journal on Learning and Advanced Education (IJOLAE), vol. 3, no. 2, pp. 96–113, Jan. 2021, doi: 10.23917/ijolae.v3i2.11754.
[31] M. D. H. Rahiem, Religion, Education, Science and Technology towards a More Inclusive and Sustainable Future. London: Routledge, 2024. doi: 10.1201/9781003322054.
[32] T. Olha, T. Kostiantyn, and T. Oleksandr, “Designing Intelligent Multi-agent Ontology-Based Training Systems: The Case of State University of Infrastructure and Technology,” 2022, pp. 181–192. doi: 10.1007/978-3-031-03877-8_16.
[33] K. Bitirgen and Ü. B. Filik, “A hybrid deep learning model for discrimination of physical disturbance and cyber-attack detection in smart grid,” International Journal of Critical Infrastructure Protection, vol. 40, p. 100582, Mar. 2023, doi: 10.1016/j.ijcip.2022.100582.
[34] K. Ratih, M. F. J. Syah, N. Nurhidayat, S. Jarin, and J. Buckworth, “Learning Patterns during the Disruptive Situation in Informal Education: Parents’ Efforts and Challenges in the Adjustment of Progressive Learning,” Indonesian Journal on Learning and Advanced Education (IJOLAE), vol. 3, no. 3, pp. 180–193, Aug. 2021, doi: 10.23917/ijolae.v3i3.15151.
[35] J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 5962–5979, Oct. 2022, doi: 10.1109/TPAMI.2021.3087709.
[36] A. Ammar, M. Ben Saada, E. Cueto, and F. Chinesta, “Casting hybrid twin: physics-based reduced order models enriched with data-driven models enabling the highest accuracy in real-time,” International Journal of Material Forming, vol. 17, no. 2, p. 16, Mar. 2024, doi: 10.1007/s12289-024-01812-4.
[37] M. S. Adhantoro et al., “Hybrid Deep-Ensemble Learning for Cybersecurity: A Multi-Dataset Framework Achieving High Precision and Minimal False Positives in Attack Detection,” International Journal of Intelligent Engineering and Systems, vol. 18, no. 8, pp. 692–706, Sep. 2025, doi: 10.22266/ijies2025.0930.42.
[38] Z. Smida, L. Cucala, A. Gannoun, and G. Durif, “A Wilcoxon-Mann-Whitney spatial scan statistic for functional data,” Comput. Stat. Data Anal., vol. 167, p. 107378, Mar. 2022, doi: 10.1016/j.csda.2021.107378.
[39] K. M. Crosman et al., “Social equity is key to sustainable ocean governance,” npj Ocean Sustainability, vol. 1, no. 1, p. 4, Aug. 2022, doi: 10.1038/s44183-022-00001-7.
[40] Y. H. Yuan, C. H. Liu, and S. S. Kuang, “Innovative interactive teaching model for cultivating digital literacy talent in decision making, sustainability, and computational thinking,” Sustainability (Switzerland), vol. 13, no. 9, 2021, doi: 10.3390/su13095117.
[41] A. A. Munshi, W. H. AlSabban, A. T. Farag, O. E. Rakha, A. Al Sallab, and M. Alotaibi, “Automated Islamic Jurisprudential Legal Opinions Generation Using Artificial Intelligence,” Pertanika J. Sci. Technol., vol. 30, no. 2, pp. 1135–1156, Mar. 2022, doi: 10.47836/pjst.30.2.16.
[42] R. Asih, D. Alonzo, and T. Loughland, “The critical role of sources of efficacy information in a mandatory teacher professional development program: Evidence from Indonesia’s underprivileged region,” Teach. Teach. Educ., vol. 118, p. 103824, Oct. 2022, doi: 10.1016/j.tate.2022.103824.
[43] J. Jiang, Q. Zhang, and Y. Hui, “The Impact of Market and Non-Market-Based Environmental Policy Instruments on Firms’ Sustainable Technological Innovation: Evidence from Chinese Firms,” Sustainability, vol. 15, no. 5, p. 4425, Mar. 2023, doi: 10.3390/su15054425.
[44] A. S. Rathor, S. Choudhury, A. Sharma, P. Nautiyal, and G. Shah, “Empowering vertical farming through IoT and AI-Driven technologies: A comprehensive review,” Heliyon, vol. 10, no. 15, p. e34998, Aug. 2024, doi: 10.1016/j.heliyon.2024.e34998.
[45] P.-C. Chang, W. Zhang, Q. Cai, and H. Guo, “Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy,” Psychol. Res. Behav. Manag., vol. Volume 17, pp. 413–427, Feb. 2024, doi: 10.2147/PRBM.S441444.
[46] A. Abirami and S. Palanikumar, “An Artificial Intelligence-based Proactive Network Forensic Framework,” Iraqi Journal of Science, pp. 5896–5911, Nov. 2023, doi: 10.24996/ijs.2023.64.11.35.
[47] S. Dutta, S. Ranjan, S. Mishra, V. Sharma, P. Hewage, and C. Iwendi, “Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms,” in 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), IEEE, Feb. 2024, pp. 1–5. doi: 10.1109/ICIPTM59628.2024.10563448.
[48] H. Wu, J. Liu, and B. Liang, “AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability,” Journal of the Knowledge Economy, vol. 16, no. 1, pp. 3826–3868, Jun. 2024, doi: 10.1007/s13132-024-01999-6.
Downloads
Submitted
Accepted
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Deva Yohand Pangestu, Laizza Natta Fatdaja, Hery Siswanto, Dzikrina Aqsha Mahardika
This work is licensed under a Creative Commons Attribution 4.0 International License.
