Cattle Weight Estimation Using Linear Regression and Random Forest Regressor

  • Anjar Setiawan Amikom Yogyakarta
  • Ema Utami Universitas Amikom Yogyakarta
  • Dhani Ariatmanto Universitas Amikom Yogyakarta
Keywords: cattle, machine learning, linear regression, random forest regressor, prediction model

Abstract

The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The dataset used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length, and chest circumference. To find out the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning.

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Published
2024-02-10
How to Cite
Anjar Setiawan, Ema Utami, & Dhani Ariatmanto. (2024). Cattle Weight Estimation Using Linear Regression and Random Forest Regressor. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 72 - 79. https://doi.org/10.29207/resti.v8i1.5494
Section
Information Systems Engineering Articles