Association Model of Temperature and Cattle Weight Influencing the Weight Loss of Cattle Due to Stress During Transportation

This study aimed to enhance animal welfare in the context of modern agriculture. The Association Rule analysis method using FP-Growth and Apriori algorithms was employed to identify patterns and factors influencing animal welfare, particularly in the context of live cattle weight loss (shrink) due to stress during transportation. Data obtained from several farms and clinical tests were used to develop insights into the relationship between farming practices, data science, and animal welfare. The research stages included data preprocessing, initial analysis, modeling, evaluation, and interpretation of results, recommendations and implications, and conclusions. The research results indicate that the use of FP-Growth and Apriori algorithms uncovered hidden patterns in the data, resulting in four association rules from FP-Growth and five rules from Apriori. These rules aid in designing recommendations to enhance animal welfare, improve agricultural efficiency, and support sustainability of the cattle sector. Our findings have significant implications in the context of animal welfare and sustainable farm management.

@article{Jaman2024,
  title     = {Association Model of Temperature and Cattle Weight Influencing the Weight Loss of Cattle Due to Stress During Transportation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jajam Haerul Jaman and Agus Buono and Dewi Apri Astuti and Sony Hartono Wijaya and Burhanuddin and Jajam Haerul Jaman},
  doi       = {10.14569/IJACSA.2024.0150154},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150154}
}