Article Sidebar

Download
PDF

Main Article Content

Abstract

Pertumbuhan permukiman yang semakin cepat di Papua berpotensi menekan ruang produksi pangan dan meningkatkan kerentanan sistem pangan daerah. Perubahan penggunaan lahan yang tidak terkendali dapat menggeser fungsi lahan pertanian, termasuk sawah, sehingga penting untuk memahami jalur dampaknya terhadap ketahanan pangan. Penelitian ini bertujuan menganalisis pengaruh pertumbuhan permukiman terhadap ketahanan pangan dengan konversi lahan sawah sebagai variabel mediasi di Papua. Penelitian menggunakan pendekatan kuantitatif dengan desain eksplanatori. Populasi penelitian adalah rumah tangga di wilayah penelitian Papua. Teknik pengambilan sampel menggunakan multistage sampling, dengan jumlah sampel sebanyak 200 responden. Data dikumpulkan melalui survei kuesioner dan dianalisis menggunakan SEM PLS dengan bantuan SmartPLS. Hasil penelitian menunjukkan bahwa pertumbuhan permukiman berpengaruh terhadap meningkatnya konversi lahan sawah, dan konversi lahan sawah berpengaruh terhadap perubahan kondisi ketahanan pangan. Selain itu, konversi lahan sawah terbukti menjadi mekanisme yang menjembatani pengaruh pertumbuhan permukiman terhadap ketahanan pangan. Implikasi penelitian ini menekankan perlunya penyelarasan kebijakan pembangunan permukiman dengan perlindungan lahan pangan serta penguatan tata kelola ruang agar ketahanan pangan Papua lebih terjaga.

Keywords

konversi lahan sawah, pertumbuhan permukiman, ketahanan pangan, SEM PLS, Papua.

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Karto, A., Irawan, S. B., & Syatfle, P. (2026). Peran Konversi Lahan Sawah dalam Memediasi Pengaruh Pertumbuhan Permukiman terhadap Ketahanan Pangan di Papua. Economics and Digital Business Review, 7(1). Retrieved from https://ojs.stieamkop.ac.id/index.php/ecotal/article/view/3580
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Akbar, A., Darma, R., Fahmid, I. M., & Irawan, A. (2023). Determinants of household food security during the COVID-19 pandemic in Indonesia. Sustainability, 15(5), 4131. DOI: 10.3390/su15054131
  2. Arif, M., & Toersilowati, R. (2024). Monitoring and predicting development of built-up area in Sleman Regency, Indonesia using multispectral imagery and CA–Markov simulation. Heliyon, 10(14), e34466. DOI: 10.1016/j.heliyon.2024.e34466
  3. Avatefi Akmal, F., & Mohammadi, Y. (2025). The nexus of agricultural land use change and food security: A comprehensive systematic review. Land Use Policy, 158, 107717. DOI: 10.1016/j.landusepol.2025.107717
  4. Cao, X., & Wang, Q. (2025). Agricultural land conversion affects food security: Evidence from data envelopment analysis and panel threshold models. Frontiers in Sustainable Food Systems, 9, 1561933. DOI: 10.3389/fsufs.2025.1561933
  5. Coates, J., Swindale, A., & Bilinsky, P. (2007). Household Food Insecurity Access Scale (HFIAS) for measurement of food access: Indicator guide (Version 3). Food and Nutrition Technical Assistance Project (FANTA).
  6. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. DOI: 10.1037/0033-2909.112.1.155
  7. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. DOI: 10.1007/BF02310555
  8. FAO. (2008). An introduction to the basic concepts of food security. Food and Agriculture Organization of the United Nations.
  9. FAO, IFAD, UNICEF, WFP, & WHO. (2023). The State of Food Security and Nutrition in the World 2023: Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum. FAO.
  10. FAO, IFAD, UNICEF, WFP, & WHO. (2024). The State of Food Security and Nutrition in the World 2024: Financing to end hunger, food insecurity and malnutrition in all its forms. FAO.
  11. Faoziyah, N., Rosyaridho, A., & Panggabean, E. (2024). Unearthing agricultural land use dynamics in Indonesia between food security and policy interventions. Land, 13(12), 2030. DOI: 10.3390/land13122030
  12. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. DOI: 10.3758/BF03193146
  13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. DOI: 10.1177/002224378101800104
  14. Giyarsih, S. R., Armansyah, Zaelany, A. A., Latifa, A., Setiawan, B., Saputra, D., Haqi, M., Lamijo, & Fathurohman, A. (2023). Interrelation of urban farming and urbanization: An alternative solution to urban food and environmental problems due to urbanization in Indonesia. Frontiers in Built Environment, 9, 1192130. DOI: 10.3389/fbuil.2023.1192130
  15. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. DOI: 10.1108/EBR-11-2018-0203
  16. Hastuti, D. S., & Yulianto, S. (2024). Factors affecting the resilience index food in Papua Province and West Papua Province using a spatial model approach. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 17(1), 121–138. DOI: 10.36456/jstat.vol17.no1.a9087
  17. Hatta, M., Rahman, A., Hidayat, T., et al. (2023). Newly-opened tidal paddy fields could make significant contributions to Indonesia rice production. Heliyon, 9(3), e13839. DOI: 10.1016/j.heliyon.2023.e13839
  18. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. DOI: 10.1007/s11747-014-0403-8
  19. Kholik, A., Nurlinda, N., Muttaqin, R., & Priyanta, M. (2024). Reformulation of policies to prevent land conversion of rice fields to support food security in Indonesia. F1000Research, 13, 945. DOI: 10.12688/f1000research.155006.1
  20. Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. DOI: 10.1111/isj.12131
  21. Mulya, S. P., Hudalah, D., Prilandita, N., & Sakti, A. (2024). Spatio-temporal changes in agricultural land and rural–urban transitions in Greater Jakarta, Indonesia. Regional Environmental Change, 24, 62. DOI: 10.1007/s10113-024-02306-4
  22. Mulya, S. P., & Hudalah, D. (2024). Agricultural intensity for sustainable regional development: A case study in peri-urban areas of Karawang Regency, Indonesia. Regional Sustainability, 5(1), 100117. DOI: 10.1016/j.regsus.2024.100117
  23. Mulyani, A., Widiatmaka, & Dariah, A. (2023). Mapping potential land reserves for food crops at national scale in Indonesia. Land, 12(5), 970. DOI: 10.3390/land12050970
  24. Nurhasan, M., Maulana, A. M., Ariesta, D. L., Usfar, A. A., Napitupulu, L., Rouw, A., Hurulean, F., Hapsari, A., Heatubun, C. D., & Ickowitz, A. (2021). Toward a sustainable food system in West Papua, Indonesia: Exploring the links between dietary transition, food security, and forests. Frontiers in Sustainable Food Systems, 5, 789186. DOI: 10.3389/fsufs.2021.789186
  25. Nurhasan, M., et al. (2024). Dietary transitions in Indonesia: The case of urban, rural, and forested areas. Food Security. DOI: 10.1007/s12571-024-01488-3
  26. Pamungkas, G., & Andriani, V. (2023). Contribution of land use change and land cover to climate change coastal areas and their impacts: Evidence in Jayapura City, Papua Province, Indonesia. Jurnal Wilayah dan Lingkungan, 11(1), 36–46. DOI: 10.14710/jwl.11.1.36-46
  27. Prayitno, G., Matsushima, K., & Jeong, H. (2021). The influence of place attachment on the decision to convert agricultural land. Heliyon, 7(7), e07546. DOI: 10.1016/j.heliyon.2021.e07546
  28. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. DOI: 10.3758/BRM.40.3.879
  29. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS.
  30. Sheng, J., et al. (2025). Monitoring paddy field abandonment and conversion with deep learning: A case study in Northeast China. Remote Sensing, 17(1), 57. DOI: 10.3390/rs17010057
  31. Sofyani, H. (2013). Modul praktik Partial Least Square (PLS) untuk penelitian akuntansi pendekatan kuantitatif. Universitas Muhammadiyah Yogyakarta.
  32. Suliman, A., Setiawan, R., Widiatmaka, Prasetyo, L. B., Rustiadi, E., & Syartinilia, N. (2023). Dynamic model to uphold rice self-sufficiency policies: An evaluation of rice field conversion in Karawang Regency, Indonesia. International Journal of Sustainable Development and Planning, 18(2), 545–557. DOI: 10.18280/ijsdp.180235
  33. Sun, D., et al. (2023). An annual paddy rice map for Southeast Asia from 2000 to 2022 at 10 m resolution based on Sentinel-1. Earth System Science Data, 15, 1501–1520. DOI: 10.5194/essd-15-1501-2023
  34. Syafiq, A., Fikawati, S., & Gemily, S. C. (2022). Household food security during the COVID-19 pandemic in urban and semi-urban areas in Indonesia. Journal of Health, Population and Nutrition, 41(1), 4. DOI: 10.1186/s41043-022-00285-y
  35. Widiastuti, R., et al. (2022). Tinjauan implementasi program food estate dan prospeknya di Merauke Papua. Forum Penelitian Agro Ekonomi, 40(2), 119–133.