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Cluster Analysis using Fuzzy Possibilistic C-Means
Clustering rice farming data to support SDGs Zero Hunger in East Java Province
ClusteringFuzzy C MeansSDGs Zero Hunger
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Python
Clustering rice farming data to support SDGs Zero Hunger in East Java Province
This project focuses on leveraging data analytics to support the Sustainable Development Goals (SDGs), particularly Zero Hunger, in East Java Province, Indonesia. By utilizing the Fuzzy Possibilistic C Means (FPCM) clustering method, the study aims to understand the patterns and characteristics of rice production across different regions.
By analyzing variables such as rice cultivation area, productivity, production volume, and rice yield, the project identifies three distinct clusters within the province. These clusters provide insights into the varying agricultural landscapes, from regions with diverse production characteristics to areas with high potential for rice production.
The findings contribute to policy recommendations aimed at enhancing agricultural development and achieving SDG Zero Hunger by improving farmer access to modern agricultural practices, expanding market opportunities, promoting agricultural research and innovation, and fostering agribusiness diversification.