Machine Learning–Based Market Value Estimation System for High-Value Local Chickens
Keywords:
Machine Learning, Price Estimation, Indigenous Chickens, Model Accuracy, K-Fold Cross-ValidationAbstract
This study aims to develop and evaluate a machine learning–based system for estimating the market value of high-value indigenous chickens to reduce subjectivity and errors associated with experience-based assessments. The dataset consists of key attributes, including breed characteristics, physical features, and market price ranges, which were subjected to data preprocessing and feature selection prior to model development. Model performance was evaluated using a 5-Fold Cross-Validation technique, with statistical metrics including Accuracy, Precision, Recall, and F1-score, together with error analysis to reflect quantitative prediction accuracy. The results indicate that the developed model can effectively classify price ranges and is suitable for practical implementation. The findings demonstrate the potential of applying artificial intelligence techniques in the valuation of specialized economic livestock and the development of decision support systems in the agricultural sector.
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