Artificial Neural Network Model to Predict Crude Protein and Crude Fiber from Physical Properties of Feedstuffs
Mohammad Miftakhus Sholikin1, Mochamad Dzaky Alifian1, Fredy Marthin Purba1, Anuraga Jayanegara2 and Nahrowi2

1 Graduate School of Nutrition and Feed Science, Faculty of Animal Science, Bogor Agricultural University, Bogor, Indonesia
2 Department of Nutrition and Feed Technology, Faculty of Animal Science, Bogor Agricultural University, Bogor, Indonesia

Abstract

The aim of this research was to build artificial neural networks model to predict crude protein and crude fiber content from physical properties of feedstuffs. The 91 data were obtained from *https://repository.ipb.ac.id* using keywords, e.g., *sifat fisik* and *pakan*. To reduce the dimensional of the data had been transformed. The independent variables consist of specific gravity (SG), bulk density (BD), compacted bulk density (CBD) and angle of repose (AoR). The dependent variable was crude protein (CP) and crude fiber (CF). Artificial neural networks (ANN) model built by R programing language 3.6.0 using library R-base and neuralnet. The correlation and accuracy used to compare predicted and actual. ANN model of crude fiber has an accuracy of 75.08% and Pearsons signification correlation (0.7529; P <0.01). ANN model of crude fiber has an accuracy of 75.08% and Pearsons signification correlation (0.7529; P <0.01). The artificial neural networks model generally can perform better to predict crude protein and crude fiber from physical properties of feedstuffs.

Keywords: Artificial neural networks model, Crude fiber, Crude protein, Physical properties, Feedstuffs

Topic: Feeds, feeding, and animal nutrition

Link: https://ifory.id/abstract-plain/QPan8C2MqwKe

Web Format | Corresponding Author (Mohammad Miftakhus Sholikin)