Pendugaan Bobot Sapi Menggunakan Algoritma Convolutional Neural Network Melalui Dimensi Ukuran Tubuh
Abstrak
The cattle population in Indonesia reaches around 18 million heads, with the majority coming from small and medium farms. The weight of the cow has an important role in determining the selling price, but small farmers often find it difficult to weigh the cattle accurately. One of the main challenges is the limitations of conventional scales. Therefore, in this study proposed a model for estimating the weight of cattle based on the dimensions of body size, namely body length and chest circumference. The body size data of the cows were taken from the Purwakarta Cattle Market UPTD, as a representation of field conditions. Modeling uses a Convolutional Neural Network (CNN) which has the ability to recognize patterns in data. This model is trained using body size data to predict cattle weight with high accuracy. The results showed that the proposed prediction model was able to provide an accurate prediction of cattle weight based on body size dimensions. In testing, the model managed to provide an estimate that is close to the true value. This modeling can be a practical solution for small farmers who have difficulty weighing cattle accurately, especially in market conditions that have limited facilities. This modeling approach opens up new opportunities in the management of livestock and the agricultural industry in general. A model for estimating cattle weight based on dimensions of body size can assist farmers in determining the selling price of cattle more accurately and efficiently, as well as making a positive contribution to increasing the productivity and welfare of livestock in Indonesia.
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