Pengembangan Model Government Decision Intelligence (IC-Gov) Untuk Mendukung Pengambilan Kebijakan Pemerintah Berbasis Data

Mohamad Jamil; IIs Hamsir Ayub Wahab; Seh Turuy

doi:10.35447/jitekh.v14i2.1377

Authors

Mohamad Jamil Progran Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun, Ternate
IIs Hamsir Ayub Wahab Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun, Ternate
Seh Turuy Progran Studi Teknik Elektro, Fakultas Teknik, Universitas Khairun, Ternate

DOI:

https://doi.org/10.35447/jitekh.v14i2.1377

Keywords:

Government Decision Intelligence, IC-Gov, Machine Learning, Predictive Insight, Regional Development Policy

Abstract

Digital government transformation requires analytical capabilities that not only process data but also generate predictive information to support policy decision-making. This study develops IC-Gov as a Government Decision Intelligence approach that integrates Machine Learning, evidence retrieval, and AI-based synthesis to support the analysis of regional development priorities. The modeling process employs four development indicators: the number of 4G blank spots, the percentage of roads in good condition, the education index, and GRDP per capita, with development priorities classified into low, medium, and high categories. Three algorithms—Random Forest, XGBoost, and Gradient Boosting—were evaluated using accuracy, macro precision, macro recall, macro F1-score, confusion matrix, and ROC-AUC. The results show that Random Forest achieved an accuracy, macro precision, macro recall, and macro F1-score of 1.00, followed by Gradient Boosting with an accuracy of 0.95 and a macro F1-score of 0.95, and XGBoost with an accuracy of 0.87 and a macro F1-score of 0.85. The ROC-AUC values were 1.000, 1.000, and 0.992, respectively. Feature importance analysis indicates that GRDP per capita has the highest relative contribution across all three models. The prediction results are subsequently integrated into IC-Gov as predictive insights, combined with supporting evidence and AI-based synthesis to generate more contextual analytical information. The findings demonstrate that integrating predictive analytics and evidence within a Government Decision Intelligence framework has the potential to strengthen data-driven regional development policy decision support while maintaining human decision-makers as the final authority

Downloads

Download data is not yet available.

References

D. Saputra, P. R. Saputra, and S. Mersa, “Transformasi Digital Pemerintahan dalam Mewujudkan Public Value,” Al-Zayn J. Ilmu Sos. Huk., pp. 2782–2795, 2026, doi: https://doi.org/10.61104/alz.v4i4.9706.

E. Yanto et al., “Pemerintahan Digital Dan Pembuatan Kebijakan Berbasis Bukti: Membangun Kerangka Kerja Tata Kelola Data Cerdas Untuk Pengambilan Keputusan Di Sektor Publik,” Indones. J. Intellect. Publ., vol. 6, no. 2, pp. 194–204, 2026, doi: 10.51577/ijipublication.

B. Adinegoro, M. Ruputhy, I. Pambudi, and T. Arrahman, “Kebijakan Satu Data Indonesia: Sebuah Antitesis Semangat Keterbukaan Dan Informasi Publik,” J. Ilmu Adm., vol. 16, pp. 1–11, 2025, doi: 10.23969/kebijakan.v16i01.21869.

Dewi, Khaidir, and Adnan, “Manajemen Pelayanan Publik Digital: Analisis Komparatif Implementasi Digital Governance Di Negara Maju Dan Negara Berkembang,” Integr. Perspect. Soc. Publick, vol. 3, no. 6, pp. 4278–4289, 2026, [Online]. Available: https://ipssj.com/index.php/ojs/article/view/1592%0Ahttps://ipssj.com/index.php/ojs/article/download/1592/1486

S. W. Mursalim and R. Fajar, “DiSH ( Digital Service Hub ): Model Transformasi Pelayanan Publik di Lingkungan Pemerintahan Kecamatan Lembang,” J. Identitas Adm. Publik Univ. Muhammadiyah Bandung, vol. 05, no. 02, pp. 104–125, 2026, doi: 10.52496/identitas.v5i2.903.

R. Sylvia, “Penggunaan AI Berbasis Large Language Models (LLM) sebagai Media Interaktif dalam Pendidikan Bahasa dan Hukum di Perguruan Tinggi,” DISIPLIN Maj. Civ. Akad. Sekol. Tinggi Ilmu Huk. Sumpah Pemuda, vol. 31, no. 4, pp. 233–242, 2025, [Online]. Available: https://ojs.stihpada.ac.id/index.php/disiplin

Dyah Adila Perdana, “Integrasi Large Language Models Dalam Pembelajaran Analisis Teks Sastra Arab Di Perguruan Tinggi,” J. Ilmu Ekon. Pendidik. dan Tek., vol. 2, no. 5, pp. 32–40, 2025, [Online]. Available: https://sihojurnal.com/index.php/identik/article/view/907

M. Murtiyoso, I. Tahyudin, and B. Berlilana, “A Systematic Review of Retrieval-Augmented Generation for Enhancing Domain-Specific Knowledge in Large Language Models,” Sinkron, vol. 9, no. 2, pp. 969–977, 2025, doi: 10.33395/sinkron.v9i2.14824.

E. Karakurt and A. Akbulut, “Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Enterprise Knowledge Management and Document Automation: A Systematic Literature Review,” Appl. Sci., vol. 16, no. 1, 2026, doi: 10.3390/app16010368.

P. Zhao et al., “Retrieval-Augmented Generation for AI-Generated Content: A Survey,” Data Sci. Eng., vol. 11, no. 1, pp. 1–29, 2026, doi: 10.1007/s41019-025-00335-5.

F. Neha, D. Bhati, and D. K. Shukla, “Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review,” 2025. doi: 10.3390/ai6090226.

Z. Li, Z. Wang, W. Wang, K. Hung, H. Xie, and F. L. Wang, “Retrieval-augmented generation for educational application: A systematic survey,” Comput. Educ. Artif. Intell., vol. 8, no. March, p. 100417, 2025, doi: 10.1016/j.caeai.2025.100417.

M. Lee, S. An, and M. S. Kim, “PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers,” Proc. 2024 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. NAACL 2024, vol. 1, pp. 6537–6555, 2024, doi: 10.18653/v1/2024.naacl-long.364.

R. F. McCloud, M. A. Bekalu, T. Vaughan, L. Maranta, E. Peck, and K. Viswanath, “Evidence for Decision-Making: The Importance of Systematic Data Collection as an Essential Component of Responsive Feedback,” Glob. Heal. Sci. Pract., vol. 11, pp. 1–7, 2023, doi: 10.9745/GHSP-D-22-00246.

A. Y. Aytar, K. Kaya, and K. Kılıç, “A synergistic multi-stage RAG architecture for boosting context relevance in data science literature,” Nat. Lang. Process. J., vol. 13, p. 100179, 2025, doi: https://doi.org/10.1016/j.nlp.2025.100179.

M. Klesel and H. F. Wittmann, “Retrieval-Augmented Generation (RAG),” Bus. Inf. Syst. Eng., vol. 67, no. 4, pp. 551–561, 2025, doi: 10.1007/s12599-025-00945-3.

Z. H. Cho et al., “Large language models in materials science: assessing RAG evaluation frameworks through graphene synthesis,” RSC Adv., vol. 16, no. 13, pp. 11306–11313, 2026, doi: https://doi.org/10.1039/d5ra09726f.

Z. Li, Z. Wang, W. Wang, K. Hung, H. Xie, and F. L. Wang, “Retrieval-augmented generation for educational application: A systematic survey,” Comput. Educ. Artif. Intell., vol. 8, p. 100417, 2025, doi: https://doi.org/10.1016/j.caeai.2025.100417.

A. James, M. Trovati, and S. Bolton, “Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers,” Appl. Sci., vol. 15, no. 11, pp. 1–17, 2025, doi: 10.3390/app15116247.

A. Salemi and H. Zamani, “Evaluating Retrieval Quality in Retrieval-Augmented Generation,” 2024, Association for Computing Machinery, Washington DC, USA. doi: 10.1145/3626772.3657957.

S. Knollmeyer et al., “Evaluating Retrieval Augmented Generation: A Comprehensive Review of Evaluation Dimensions, Question Types, and Application,” SN Comput. Sci., vol. 7, no. 5, 2026, doi: 10.1007/s42979-026-05134-x.

L. R. Sitompul, A. A. Nababan, M. L. Manihuruk, W. A. Ponsen, and S. Supriyandi, “Comparison of Xgboost, Random Forest and Logistic Regression Algorithms in Stroke Disease Classification,” Sinkron, vol. 9, no. 2, pp. 957–968, 2025, doi: 10.33395/sinkron.v9i2.14794.

J. Montomoli et al., “Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients,” J. Intensive Med., vol. 1, no. 2, pp. 110–116, 2021, doi: 10.1016/j.jointm.2021.09.002.

G. M. Foody, “Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient,” PLoS One, vol. 18, no. 10 October, pp. 1–27, 2023, doi: 10.1371/journal.pone.0291908.

S. Riyanto, I. S. Sitanggang, T. Djatna, and T. D. Atikah, “Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 6, pp. 1082–1090, 2023, doi: 10.14569/IJACSA.2023.01406116.