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Submitted
Dec 30, 2022
Accepted
Dec 22, 2024
Published
Dec 30, 2024
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Abstract

Agriculture plays a strategic role in achieving food security in Indonesia. However, the production of major food crops in Indonesia shows uneven distribution, which may affect efforts to achieve food self-sufficiency. This study aims to cluster 34 provinces in Indonesia based on the total production of seven major food crops (rice, corn, soybean, mung bean, peanut, cassava, and sweet potato) using the Spatial Fuzzy C-Means (sFCM) method. Cluster validation using Modified Partition Coefficient (MPC) and Partition Entropy (PE) shows that the clustering results have high membership clarity and low entropy, making them relevant for spatial data analysis. The findings highlight the unequal distribution of food crop production and provide policy recommendations, where the first cluster can be optimized as a national food production hub, while the second cluster requires interventions based on infrastructure, technology, and redistribution policies.  This research makes an important contribution in providing a data-driven scientific basis for food production equity planning. The sFCM method used demonstrates effective capabilities in analyzing data with spatial elements, supporting more inclusive policies for the improvement of national food security and the achievement of sustainable development goals in Indonesia. 

Keywords

clustering, main food crop product, spatial fuzzy c-means

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

How to Cite
1.
Yozza H, Aldi Mukhlis, Maiyastri. Clustering Provinces in Indonesia Based on The Main Food Crop Production Using The Spatial Fuzzy C-Means. EKSAKTA [Internet]. 2024Dec.30 [cited 2025Apr.4];25(04):498-507. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/385

References

  1. Adegbeye, M. J., Reddy, P. R. K., Obaisi, A. I., Elghandour, M. M. M. Y., Oyebamiji, K. J., Salem, A. Z. M., ... & Camacho-Díaz, L. M. (2020). Sustainable agriculture options for production, greenhouse gasses and pollution alleviation, and nutrient recycling in emerging and transitional nations-An overview. Journal of Cleaner Production, 242, 118319.
  2. Rusliyadi, M., Jamil, A. B. H. M., Othman, M., & Kumalasari, R. T. (2018). Agricultural extension policy, agricultural growth and poverty reduction in Indonesia. International Journal of Engineering & Technology, 7(4), 5539-5550.
  3. Septiani, Y., & Iskandar, D. D. (2024). Determinants Of Food Security Among Indonesian Farming Households. Jurnal REP (Riset Ekonomi Pembangunan), 9(2), 208-213.
  4. Valerio, E., Hilmiati, N., Prior, J., & Dahlanuddin, D. (2022). Analysis of the agricultural innovation system in Indonesia: A case study of the beef sector in Nusa Tenggara Barat. Agricultural Systems, 203, 103529.
  5. Riptanti, E. W., Masyhuri, M., Irham, I., Suryantini, A., & Mujiyo, M. (2018). The development of leading food commodities based on local wisdom in food-insecure area in East Nusa Tenggara Province, Indonesia. Applied Ecology & Environmental Research, 16(6).
  6. Rozi, F., Elisabeth, D. A. A., Krisdiana, R., Adri, A., Yardha, Y., & Rina, Y. (2022). Prospects of cassava development in Indonesia in supporting global food availability in future. In Advances in Root Vegetables Research. IntechOpen.
  7. Gocht, A., & Röder, N. (2014). Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany. International Journal of Geographical Information Science, 28(9), 1744-1764.
  8. Zhang, H., Zhang, J., & Song, J. (2022). Analysis of the threshold effect of agricultural industrial agglomeration and industrial structure upgrading on sustainable agricultural development in China. Journal of Cleaner Production, 341, 130818.
  9. Elkhateeb, E., Soliman, H., Atwan, A., Elmogy, M., Kwak, K. S., & Mekky, N. (2021). A novel coarse-to-Fine Sea-land segmentation technique based on Superpixel fuzzy C-means clustering and modified Chan-Vese model. IEEE Access, 9, 53902-53919.
  10. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2-3), 191-203.
  11. Chuang, K. S., Tzeng, H. L., Chen, S., Wu, J., & Chen, T. J. (2006). Fuzzy c-means clustering with spatial information for image segmentation. computerized medical imaging and graphics, 30(1), 9-15.
  12. Amri, F. U. (2018). Implementasi Segmentasi Spatial Fuzzy C-Means Pada Identifikasi Citra Daging Sapi dan Babi. In Seminar Nasional Teknologi Informasi Komunikasi dan Industri (pp. 206-214).
  13. Xie, X. L., & Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(08), 841-847.
  14. Isnaini, S. A., Indah, P. N., & Laily, D. W. (2024). The Analysis of Factors Influencing Rice Imports in Indonesia. Buletin Penelitian Sosial Ekonomi Pertanian Fakultas Pertanian Universitas Haluoleo, 26(2), 105-115.
  15. Sujianto, Gunawan, E., Saptana, Syahyuti, Darwis, V., Ashari, ... & Marhendro. (2022). Farmers’ perception, awareness, and constraints of organic rice farming in Indonesia. Open Agriculture, 7(1), 284-299.
  16. Lybbert, T. J., & Sumner, D. A. (2012). Agricultural technologies for climate change in developing countries: Policy options for innovation and technology diffusion. Food policy, 37(1), 114-123.
  17. Peterman, A., Behrman, J. A., & Quisumbing, A. R. (2014). A review of empirical evidence on gender differences in nonland agricultural inputs, technology, and services in developing countries (pp. 145-186). Springer Netherlands.
  18. Gwambene, B., Liwenga, E., & Mung’ong’o, C. (2023). Climate change and variability impacts on agricultural production and food security for the smallholder farmers in Rungwe, Tanzania. Environmental Management, 71(1), 3-14.
  19. Bekhit, A. E. D. A., Riley, W. W., & Hussain, M. A. (Eds.). (2022). Alternative Proteins: Safety and Food Security Considerations. CRC Press.
  20. Rob, V. O. S., & Cattaneo, A. (2021). Poverty reduction through the development of inclusive food value chains. Journal of Integrative Agriculture, 20(4), 964-978.
  21. Sreevalsan-Nair, J. (2022). Fuzzy C-Means Clustering. In Encyclopedia of mathematical geosciences (pp. 1-3). Cham: Springer International Publishing.
  22. Antoine, V., Guerrero, J. A., & Romero, G. (2022). Possibilistic fuzzy c-means with partial supervision. Fuzzy Sets and Systems, 449, 162-186.
  23. Wang, H. Y., Wang, J. S., & Wang, G. (2022). A survey of fuzzy clustering validity evaluation methods. Information Sciences, 618, 270-297.
  24. Cheon, J. H., Kim, D., & Park, J. H. (2019, August). Towards a practical cluster analysis over encrypted data. In International Conference on Selected Areas in Cryptography (pp. 227-249). Cham: Springer International Publishing.
  25. Bui, Q. T., Vo, B., Snasel, V., Pedrycz, W., Hong, T. P., Nguyen, N. T., & Chen, M. Y. (2020). SFCM: A fuzzy clustering algorithm of extracting the shape information of data. IEEE Transactions on Fuzzy Systems, 29(1), 75-89.
  26. Safouan, S., El Moutaouakil, K., & Patriciu, A. M. (2024). Fractional Derivative to Symmetrically Extend the Memory of Fuzzy C-Means. Symmetry, 16(10), 1353.
  27. Klimczuk, A., & Klimczuk-Kochańska, M. (2023). Core-periphery model. In The palgrave encyclopedia of global security studies (pp. 239-245). Cham: Springer International Publishing.
  28. Shepley, S., & Wilmot, J. (1995). Core vs. periphery. Behind the myth of European Union, 51-82.
  29. Pawlak, K., & Kołodziejczak, M. (2020). The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production. Sustainability, 12(13), 5488.
  30. Hatab, A. A., Cavinato, M. E. R., Lindemer, A., & Lagerkvist, C. J. (2019). Urban sprawl, food security and agricultural systems in developing countries: A systematic review of the literature. Cities, 94, 129-142.