OPTIMIZATION OF LAYING DUCKS FEED COMPOSITION USING THE K-MEANS CLUSTERING ALGORITHM METHOD IN SECANG SUBDISTRICT
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Abstract
Indonesia has significant potential in duck farming, particularly as a source of eggs and meat. However, the productivity of local laying ducks remains low due to the traditional feed management practices still widely used. In Secang District, Magelang Regency, farmers often determine feed composition based on availability and peer recommendations without proper consideration of nutritional requirements. This leads to imbalanced nutrition, negatively affecting egg production. This study aims to provide optimal feed composition recommendations using the K-Means Clustering algorithm. The algorithm clusters feed data based on nutritional content and egg production performance. Through this approach, farmers are expected to gain more accurate and efficient information in determining feed composition, thereby improving productivity, reducing operational costs, and enhancing product quality. Furthermore, this research contributes to the development of knowledge in both information technology and animal husbandry by applying machine learning techniques in the agricultural sector.
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