PREDICTIVE MODELING OF BROILER CHICKEN PRODUCTION USING THE NAIVE BAYES CLASSIFICATION ALGORITHM
Abstract
Serious challenges are faced by broiler chicken farmers in Seumirah Village, Nisam Antara Subdistrict, North Aceh Regency, in their efforts to create high-quality and productive chickens. These difficulties not only impact the farmers' income but also result in recurring losses every year. This research aims to design a system using the Naive Bayes Classifier algorithm to assess the capacity and classify production types based on specific criteria such as population, age, depletion, FCR (Feed Conversion Ratio), IP (Index Performance), and BW (Body Weight). The system aims to classify broiler chicken production as either increasing (profitable) or decreasing (unprofitable). In the development of this predictive system, the PHP programming language is employed, with a MySQL database as the data storage medium. The results of this broiler chicken production prediction system have proven effective in providing information in the form of profit or loss reports based on the harvest results for each monthly period. The implementation of this system is expected to assist in optimizing farmers' production management, increasing business profitability, and providing better guidance for future business decisions. The classification results using the Naive Bayes method indicate an accuracy rate of 86,67 and error rate of 13,3%.
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