The Machine Learning has been identified as a promising approach to
knowledge-based system development. This study aims to examine the ability of
machine learning techniques for farmer’s decision making and to develop the
reference model for using pig farm data. We compared five machine learning
techniques: logistic regression, decision tree, artificial neural network, k-nearest
neighbor, and ensemble. All models are well performed to predict the sow’s
productivity in all parity, showing over 87.6% predictability. The model predictability
of total litter size are highest at 91.3% in third parity and decreasing as parity
increases. The ensemble is well performed to predict the sow’s productivity. The
neural network and logistic regression is excellent classifier for all parity. The
decision tree and the k-nearest neighbor was not good classifier for all parity.
Performance of models varies over models used, showing up to 104% difference
in lift values. Artificial Neural network and ensemble models have resulted in
highest lift values implying best performance among models.