Main Article Content
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
Article Details
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.3];25(04):498-507. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/385
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