Article Sidebar

Download
PDF
Statistic
Read Counter : 1 Download : 2

Main Article Content

Abstract

This study aims to formulate a Big Data and Artificial Intelligence (AI)-based model for monitoring the SDGs within a data-driven governance framework. Using a descriptive qualitative approach and a conceptual study, the study focuses on four aspects: current monitoring mechanisms, potential Big Data-based data sources, opportunities for AI utilization, and data governance and inter-agency interoperability. Data were obtained from literature reviews, policy documents, and news sources on the implementation of the SDGs, Big Data, AI, and data governance. The results show that the weakness of SDGs monitoring lies not in the availability of indicators or data, but rather in the monitoring and governance architecture, which remains administrative, periodic, and sectoral, resulting in slow information flow and minimal analytics. Abundant data sources actually meet the characteristics of Big Data, and AI can transform indicator readings into real-time, analytical, and predictive insights. However, their utilization has not been optimal due to interoperability constraints and procedural data governance. Monitoring transformation requires the integration of Big Data, the use of AI, and a shift in governance towards interoperability across agencies. Further research suggestions include the empirical testing of the model through case studies in local governments/agencies, the development of a Big Data and AI-based monitoring dashboard prototype, and the measurement of its impact on the quality of policy-making

Keywords

Big Data Artificial Intelligence (AI) SDGs Monitoring Data Goevernance Interoperability

Article Details

Author Biographies

Yujitia Ahdarrijal

Administrasi Negara, Universitas Putera Batam

Nur Syakiran Akmal Ismail Syakiran Akmal Ismail

Pusat Pengajian Kerajaan Polisi, Universiti Utara Malaysia

References

  1. Abdalrahman, A., Häfner, Y.-J., Maibach, P., & Haederli, C. (2022). A Way Forward to Achieve Interoperability in Multi-Vendor HVDC Systems. 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141619147&partnerID=40&md5=e3e9a18abbe077d6d4bb1a0c1d51f82f
  2. Acharya, S., Wintercorn, O., Tripathy, A., Hanif, M., Van Deventer, J., & Päivärinta, T. (2023). Twins Interoperability through Service Oriented Architecture: A use-case of Industry 4.0. In K. J., S. of E. S. LUT University Lappeenranta Campus, Lappeenranta, P. T., & P. T. (Eds.), CEUR Workshop Proceedings (Vol. 3506, pp. 107–115). CEUR-WS. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175789572&partnerID=40&md5=22e56474ce406fd03b8101bbef432b8d
  3. Ady Thea. (2022). Kemnaker Perkuat Kebijakan Satu Data Ketenagakerjaan. https://www.hukumonline.com/berita/a/kemnaker-perkuat-kebijakan-satu-data-ketenagakerjaan-lt62da701ce845a/
  4. Aji, M. Q. W. (2020). Bureaucratic Reform: A Case Study in Secretariat General of the Ministry of Education and Culture. Jurnal Ilmiah Ilmu Administrasi Publik, 9(2), 203. https://doi.org/10.26858/jiap.v9i2.10799
  5. Alghalbie, F., Elnaem, M. H., & McCarron, P. A. (2026). Perspectives on artificial intelligence use in pharmacy education in Northern Ireland: A qualitative study based on the unified theory of acceptance and use of technology. Currents in Pharmacy Teaching and Learning, 18(1), 102490. https://doi.org/https://doi.org/10.1016/j.cptl.2025.102490
  6. Anam, K. (2021). Strategi Kemnaker Bangun Satu Data Ketenagakerjaan di Pusat dan Daerah. Detik.Com. https://finance.detik.com/berita-ekonomi-bisnis/d-5749411/strategi-kemnaker-bangun-satu-data-ketenagakerjaan-di-pusat-dan-daerah
  7. Anser, M. K., & Adebayo, T. S. (2026). AI-driven renaissance: Revolutionizing technology through industrial artificial intelligence in European economies. Technology in Society, 84, 103129. https://doi.org/https://doi.org/10.1016/j.techsoc.2025.103129
  8. Ardani, I. (2022). Tantangan Kebijakan Satu Data Indonesia: Studi Kasus Pada Sistem Pencatatan Kematian dan Penyebab Kematian di Dki Jakarta Indonesia ’ S One Data Policy Challenge : A Case Study on The Death and Cause Of Death Registration System in Jakarta. Buletin Penelitian Sistem Kesehatan, 25(1), 52–60.
  9. Arnoldus Kristianus. (2021). Kemnaker Konsolidasi Kebijakan Satu Data Ketenagakerjaan Pusat dan Daerah. Investor.Id. https://investor.id/business/253664/kemnaker-konsolidasi-kebijakan-satu-data-ketenagakerjaan-pusat-dan-daerah
  10. Balci, N., & Dogan, M. (2026). Artificial intelligence, clean energy, and market integration: Evidence from multi-period quantile dynamics. Journal of Environmental Management, 397, 128301. https://doi.org/https://doi.org/10.1016/j.jenvman.2025.128301
  11. Bappeda.Kaltimprov. (2021). Konsolidasi Nasional Satu Data Ketenagakerjaan. https://bappeda.kaltimprov.go.id/postingan/konsolidasi-nasional-satu-data-ketenagakerjaan
  12. Bappeda Kaltim. (2022). Rakornas Satu Data Ketenagakerjaan Tahun 2022. Bappeda Prov Kalimantan Timur. https://bappeda.kaltimprov.go.id/postingan/rakornas-satu-data-ketenagakerjaan-tahun-2022
  13. Bappenas. (2022). Peluncuran data.go.id, Portal Satu Data Indonesia untuk Bagi Pakai Data. https://www.bappenas.go.id/id/berita/peluncuran-datagoid-portal-satu-data-indonesia-untuk-bagi-pakai-data-b2Xnt
  14. Burton, J. (2023). Algorithmic extremism? The securitization of artificial intelligence (AI) and its impact on radicalism, polarization and political violence. Technology in Society, 75. https://doi.org/10.1016/j.techsoc.2023.102262
  15. Çakir, S. K., & Çeti̇nkaya, Ş. Ş. (2026). The effect of an artificial intelligence- supported screen-based simulation on breast self-examination in nursing students: A sequential mixed-methods study. Nurse Education in Practice, 92, 104757. https://doi.org/https://doi.org/10.1016/j.nepr.2026.104757
  16. Che, S., Liu, C., Wang, J., & Wang, J. (2026). Can artificial intelligence drive enterprise green management innovation? A new perspective on harnessing intelligence. Technology in Society, 85, 103156. https://doi.org/https://doi.org/10.1016/j.techsoc.2025.103156
  17. Damhuis, C., Kraus, D., Covic, G. A., Herzog, H.-G., Lawton, P. A. J., & Lin, F. J. (2023). Impedance Plane Based Interoperability Assessment of Two High-Power 50 kW WPT Systems for EV Charging. 2023 IEEE Wireless Power Technology Conference and Expo, WPTCE 2023 - Proceedings. https://doi.org/10.1109/WPTCE56855.2023.10216061
  18. Dinnakerind. (2021). Rapat Koordinasi Teknis Penyelenggaraan Satu Data Ketenagakerjaan. https://dinnakerind.demakkab.go.id/rapat-koordinasi-teknis-penyelenggaraan-satu-data-ketenagakerjaan/
  19. Efthymiou, M., McCarthy, K., Markou, C., & O’connell, J. F. (2022). An Exploratory Research on Blockchain in Aviation: The Case of Maintenance, Repair and Overhaul (MRO) Organizations. Sustainability (Switzerland), 14(5), 1–17. https://doi.org/10.3390/su14052643
  20. Eman Sulaiman, Cucu Handayani, & Susi Widyastuti. (2021). Transformasi Digital Technology-Organization-Environment (Toe) Dan Inovasi Difusi E-Business Untuk Umkm Yang Berkelanjutan: Model Konseptual. Jurnal Manajemen & Bisnis Kreatif, 7(1), 51–62. https://doi.org/10.36805/manajemen.v7i1.1947
  21. Fabiola Febrinastri. (2022). Menaker Kenalkan Portal Satu Data Ketenagakerjaan dan SMData di Rakornas SDK. Suara.Com. https://www.suara.com/bisnis/2022/07/22/094016/menaker-kenalkan-portal-satu-data-ketenagakerjaan-dan-smdata-di-rakornas-sdk?page=all
  22. Forney, J., & Epiney, L. (2022). Governing Farmers through data? Digitization and the Question of Autonomy in Agri-environmental governance. Journal of Rural Studies, 95, 173–182. https://doi.org/https://doi.org/10.1016/j.jrurstud.2022.09.001
  23. Fu, L., Jin, L., & Li, Y. (2026). Artificial intelligence applications and innovation ambidexterity: The “inverted U-shaped” regulating effect of risk-taking. Information Processing & Management, 63(4), 104635. https://doi.org/https://doi.org/10.1016/j.ipm.2026.104635
  24. Gravili, G., Hassan, R., Avram, A., & Schiavone, F. (2023). Big data and human resource management: paving the way toward sustainability. European Journal of Innovation Management, 26(7), 552–590. https://doi.org/10.1108/EJIM-01-2023-0048
  25. Hamadou, I., Wicaksono, T. Y., & Hamidi, M. L. (2026). Does artificial intelligence reduce carbon emissions in OECD and BRICS? Examining the role of research and development. Journal of Environmental Management, 398, 128500. https://doi.org/https://doi.org/10.1016/j.jenvman.2025.128500
  26. Ida Fauziah. (2020). Launching Satu Data Ketenagakerjaan. Kementerian Ketenagakerjaan RI. https://www.youtube.com/live/UODNDDJRTrU?feature=share
  27. Jambi.Prov.go.id. (2022). Kemnaker Perkuat Implementasi Satu Data Ketenagakerjaan Pusat dan Daerah. https://diskominfo.jambiprov.go.id/berita/berita_detail/89
  28. Jelita, I. N. (2020). Menaker Jelaskan Tujuan Dibuatnya Satu Data Ketenagakerjaan. Media Indonesia. https://mediaindonesia.com/ekonomi/358800/menaker-jelaskan-tujuan-dibuatnya-satu-data-ketenagakerjaan
  29. Jpnn.com. (2021). Kemnaker Targetkan Penerapan Satu Data Ketenagakerjaan Dimulai Tahun Depan. https://m.jpnn.com/news/kemnaker-targetkan-penerapan-satu-data-ketenagakerjaan-dimulai-tahun-depan?page=3
  30. Kaltimprov.go.id. (2021). Konsolidasi Nasional Satu Data Ketenagakerjaan. https://bappeda.kaltimprov.go.id/postingan/konsolidasi-nasional-satu-data-ketenagakerjaan
  31. KAVAK, A., & Gültekin, V. (2026). Examining the relationship between digital competencies and artificial intelligence literacy of academic librarians in Türkiye. The Journal of Academic Librarianship, 52(1), 103191. https://doi.org/https://doi.org/10.1016/j.acalib.2025.103191
  32. Kementerian PPN/Bappenas. (2022). Peluncuran data.go.id, Portal Satu Data Indonesia untuk Bagi Pakai Data. https://www.bappenas.go.id/berita/peluncuran-datagoid-portal-satu-data-indonesia-untuk-bagi-pakai-data-b2Xnt
  33. Kemnaker.go.id. (2021). Kemnaker Perkuat Konsolidasi Kebijakan Satu Data Ketenagakerjaan Di Pusat dan Daerah. https://kemnaker.go.id/news/detail/kemnaker-perkuat-konsolidasi-kebijakan-satu-data-ketenagakerjaan-di-pusat-dan-daerah
  34. Kemnaker.go.id. (2022). Satu Data Ketenagakerjaan. https://satudata.kemnaker.go.id/
  35. Kominfo.go.id. (2020). Kemnaker Luncurkan Satu Data Ketenagakerjaan. https://www.kominfo.go.id/content/detail/30589/kemnaker-luncurkan-satu-data-ketenagakerjaan/0/berita
  36. Kominfo.go.id. (2021). Dinas Kominfo Adakan Pelatihan Aplikasi Satu Data Daerah. https://kominfo.kulonprogokab.go.id/detil/458/dinas-kominfo-adakan-pelatihan-aplikasi-satu-data-daerah
  37. Krúpová, S., & Koman, G. (2026). The Role of IoT and Big Data in Smart City Transport Management: Lessons Learned from Case Studies in Slovakia and the EU. Transportation Research Procedia, 93, 948–959. https://doi.org/https://doi.org/10.1016/j.trpro.2025.12.027
  38. Leńko-Szymańska, A. (2026). Not so fast? A comparative study of pre-service teachers’ lesson design using corpora and generative artificial intelligence. Applied Corpus Linguistics, 6(1), 100168. https://doi.org/https://doi.org/10.1016/j.acorp.2025.100168
  39. Leski Rizkinaswara. (2020). Menaker: Satu Data Ketenagakerjaan Wujudkan Interoperabilitas Data. Kominfo.Go.Id. https://aptika.kominfo.go.id/2020/11/menaker-satu-data-ketenagakerjaan-wujudkan-interoperabilitas-data/
  40. Ma, J., Wu, J., Li, Z., & Deng, K. (2026). Can government digital incentives enhance enterprise artificial intelligence capability? - Evidence from China. Technology in Society, 86, 103258. https://doi.org/https://doi.org/10.1016/j.techsoc.2026.103258
  41. Maulidya, R., Rozikin, M., Ilmu, F., Universitas, A., Timur, J., & Publik, K. (2022). Analisis retrospektif kebijakan satu data indonesia. 9, 6–8.
  42. Menpan.go.id. (2022). Sekda Adi Arnawa Hadiri Rakornas Satu Data Ketenagakerjaan Tahun 2022. https://sippn.menpan.go.id/berita/detil/dinas-komunikasi-dan-infromatika/sekda-adi-arnawa-hadiri-rakornas-satu-data-ketenagakerjaan-tahun-2022
  43. Muhammad Firdaus. (2022). Implementasi kebijakan satu data dalam menyediakan basis data yang akurat dan transparan di provinsi sumatera selatan. Skripsi.
  44. Mukhlis, I. R., Hayam, U., Perbanas, W., Pipin, S. J., & Mikroskil, U. (2024). BIG DATA ( Mengenal Big Data & Implementasinya di Berbagai Bidang ) (Issue February).
  45. Park, M. S., & Park, H. (2019). An examination of metadata practices for research data reuse: Characteristics and predictive probability of metadata elements. In Malaysian Journal of Library and Information Science (Vol. 24, Issue 3, pp. 61–75). https://doi.org/10.22452/mjlis.vol24no3.4
  46. Power, N., Alcock, J., Philpot, R., & Levine, M. (2024). The psychology of interoperability: A systematic review of joint working between the UK emergency services. Journal of Occupational and Organizational Psychology, 97(1), 233–252. https://doi.org/10.1111/joop.12469
  47. Pribadi, U. (2021). Bureaucratic reform, public service performance, and citizens’ satisfaction: The case of Yogyakarta, Indonesia. Public Policy and Administration, 20(2), 312–326. https://doi.org/10.13165/VPA-21-20-2-13
  48. Pronello, C. (2026). Artificial intelligence and its impact on the current and future transport workforce: policies and research to bridge the gaps and foster cooperation versus competition between countries. Transportation Research Interdisciplinary Perspectives, 36, 101802. https://doi.org/https://doi.org/10.1016/j.trip.2025.101802
  49. Rizky Maulidya, M. R. (2022). Analisis retrospektif kebijakan satu data indonesia. Dinamika : Jurnal Ilmiah Ilmu Administrasi Negara, 9, 6–8.
  50. Sáez-Hernández, R., Cruz, J., Alcalà-Bernàrdez, M., Morales-Rubio, Á., & Cervera, M. L. (2025). An artificial intelligence-based semiquantitative method based on visible spectroscopy and imaging to analyse inorganic red pigments in wall paintings. Journal of Cultural Heritage, 75, 139–146. https://doi.org/https://doi.org/10.1016/j.culher.2025.07.018
  51. Sampaio, A. Z., & Gomes, A. M. (2021). BIM Structural Project Applied in A Case Study: Interoperability Analyses, Reinforcement Detailing Drawings and Quantity Take-off. Open Construction and Building Technology Journal, 15, 196–213. https://doi.org/10.2174/1874836802115010196
  52. Samuel, J., Kashyap, R., Samuel, Y., & Pelaez, A. (2022). Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations. International Journal of Information Management, 65. https://doi.org/10.1016/j.ijinfomgt.2022.102505
  53. Sandoval-Almazan, R., Millan-Vargas, A. O., & Garcia-Contreras, R. (2024). Examining public managers’ competencies of artificial intelligence implementation in local government: A quantitative study. Government Information Quarterly, 41(4), 101986. https://doi.org/https://doi.org/10.1016/j.giq.2024.101986
  54. Setda Kab.Bandung. (2022). Sekda Adi Arnawa Hadiri Rakornas Satu Data Ketenagakerjaan Tahun 2022. https://setda.badungkab.go.id/berita/43124-sekda-adi-arnawa-hadiri-rakornas-satu-data-ketenagakerjaan-tahun-2022
  55. Skog, J., Rafie, S., Schnock, K. O., Yoon, C., Lipsitz, S., & Lew, P. (2022). The Impact of Smart Pump Interoperability on Errors in Intravenous Infusion Administrations: A Multihospital before and after Study. Journal of Patient Safety, 18(3), E666–E671. https://doi.org/10.1097/PTS.0000000000000905
  56. Sonkamble, R. G., Phansalkar, S. P., Potdar, V. M., & Bongale, A. M. (2021). Survey of Interoperability in Electronic Health Records Management and Proposed Blockchain Based Framework: MyBlockEHR. IEEE Access, 9, 158367–158401. https://doi.org/10.1109/ACCESS.2021.3129284
  57. Stöckl, H., Fabbri, C., Cook, H., Galez-Davis, C., Grant, N., Lo, Y., Kiss, L., & Zimmerman, C. (2021). Human trafficking and violence: Findings from the largest global dataset of trafficking survivors. Journal of Migration and Health, 4. https://doi.org/10.1016/j.jmh.2021.100073
  58. Sugiyono. (2017). Metode Penelitian Kuantitatif, Kualitatif, R & D. Bandung: CV Alfabeta.
  59. Sürme, Y., Topan, H., & Maraş Baydoğan, G. (2026). A new era in the education and practice of nurse academics: A qualitative study on the use of artificial intelligence. Nurse Education in Practice, 90, 104649. https://doi.org/https://doi.org/10.1016/j.nepr.2025.104649
  60. Tian, Y., Zhu, D., Nazar, R., Ali, S., & Mirza, A. (2026). From algorithms to access: Role of artificial intelligence in revolutionizing financial inclusion. Technology in Society, 84, 103098. https://doi.org/https://doi.org/10.1016/j.techsoc.2025.103098
  61. Wang, C., Yin, Y., & Hu, H. (2026). The Rise of Algorithmic Governance and the Dual Revolution: Applications, Challenges, and Governance of Artificial Intelligence in Public Administration. Technology in Society, 103264. https://doi.org/https://doi.org/10.1016/j.techsoc.2026.103264
  62. Wang, W., Zhang, H., Sun, Z., Wang, L., Zhao, J., & Wu, F. (2023). Can digital policy improve corporate sustainability? Empirical evidence from China’s national comprehensive big data pilot zones. Telecommunications Policy, 47(9), 102617. https://doi.org/https://doi.org/10.1016/j.telpol.2023.102617
  63. Wibowati, J. (2021). Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Pada Pt Muarakati Baru Satu Palembang. Jurnal Manajemen, 8(2), 15–31. https://doi.org/10.36546/jm.v8i2.348
  64. Wu, Q., Chen, L., Chen, M., & Huang, Y. (2026). Exploring the impact of artificial intelligence on business talent development in higher education:A systematic literature review and research agenda. The International Journal of Management Education, 24(1), 101287. https://doi.org/https://doi.org/10.1016/j.ijme.2025.101287
  65. Xia, W., Meng, G., Zhang, H., & Wang, S. (2026). Study on the regional image of China (Tianjin) pilot free trade zone from the perspective of news big data. Cities, 171, 106790. https://doi.org/https://doi.org/10.1016/j.cities.2026.106790
  66. Xu, B. (2026). Artificial intelligence and green technology innovation: Evidence from 265 cities in China. Journal of Environmental Management, 398, 128494. https://doi.org/https://doi.org/10.1016/j.jenvman.2025.128494
  67. Yu, H., & He, X. (2021). Corporate data sharing, leakage, and supervision mechanism research. Sustainability (Switzerland), 13(2), 1–15. https://doi.org/10.3390/su13020931
  68. Yuen, K. F., Cai, L., Qi, G., & Wang, X. (2021). Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory. Technology Analysis and Strategic Management, 33(5), 505–519. https://doi.org/10.1080/09537325.2020.1826423
  69. Yulfitri, A. (2020). Analisis Data Governance Maturity Menggunakan Standford Data Governance Maturity. BRITech (Jurnal Ilmiah Ilmu Komputer, Sains Dan Teknologi Terapan), 1(2), 1–10.
  70. Zaitseva, N. V, Alekseev, V. B., Luzhetskiy, K. P., Kiryanov, D. A., Ustinova, O. Y., & Nosov, A. E. (2023). The development of the database of the technologies for prevention of communicable diseases considering specific workplaces. Profilakticheskaya Meditsina, 26(4), 20–25. https://doi.org/10.17116/profmed20232604120
  71. Zhang, D., Pee, L. G., Pan, S. L., & Liu, W. (2022). Orchestrating artificial intelligence for urban sustainability. Government Information Quarterly, 39(4), 101720. https://doi.org/https://doi.org/10.1016/j.giq.2022.101720
  72. Zhu, Y. (2020). Open-access policy and data-sharing practice in UK academia. Journal of Information Science, 46(1), 41–52. https://doi.org/10.1177/0165551518823174

DB Error: Unknown column 'Array' in 'where clause'