Analysis of Broiler Chicken Production Success Classification Using K-Nearest Neighbors And Naive Bayes Methods at PT. Jandela Jaga Kaloka (Jajaka)

The livestock subsector, particularly broiler chickens, provides animal protein sources in Indonesia. However, low production efficiency, managerial challenges, and productivity fluctuations remain the primary obstacles to achieving sustainability in this sector. This study aims to analyze the success rate of broiler chicken production at PT. Jandela Jaga Kaloka (JAJAKA) using a data mining classification approach with the K-Nearest Neighbors (K-NN) and Naive Bayes algorithms. The research population comprises broiler production data from various branches of PT. JAJAKA, with a sample of 200 datasets selected based on representative criteria. The study employs the hold-out method with data splits of 60:40 and 70:30 for training and testing the models. The success rate of production is classified into three categories: good, less good, and excellent. The findings reveal that the K-NN algorithm outperforms with an accuracy of 92.59%, compared to Naive Bayes, which achieves 76.67%. Regarding recall, K-NN records a value of 96.67%, higher than Naive Bayes at 71.67%. However, Naive Bayes shows slightly better precision (94.29%) than K-NN (93.55%). These results affirm that the K-NN algorithm is more effective for classifying the success rate of broiler chicken production, supporting PT. JAJAKA in making more precise and strategic managerial decisions. Furthermore, this study contributes significantly to developing data mining methods in the poultry farming sector to improve efficiency and productivity sustainably. It provides valuable insights for PT. Jandela Jaga Kaloka will evaluate the success rate of broiler chicken production, facilitating more accurate managerial decision-making.