Year: 2024
Type: article
Abstract: Rice growth is a crucial indicator in determining the success of the agricultural sector, especially in agrarian regions like Aceh. This study aims to cluster rice growth patterns using the K-Means method, a widely recognized and efficient clustering technique. The analyzed data include land area, harvest yield, rice varieties, and environmental factors such as rainfall, temperature, and soil fertility. By applying the K-Means method, the data were grouped into several clusters representing the levels of rice growth across different regions. The results revealed significant differences in rice productivity among clusters, influenced by environmental factors and agricultural management. These findings provide strategic insights for decision-making, particularly in resource optimization and policy planning, to sustainably enhance rice growth outcomes in Aceh.
Authors: Abdul Sidik, Haekal Ilmandry, Hendrik Asta Manggala, Reza Aqib Setyoanggoro
Institution: Universitas Pamulang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W7089636137
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