Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera

Abstract

Laying hens eggs are one of the livestock commodities that make a very large contribution to the supply of eggs as a community need. Therefore, it is necessary to predict the egg production of laying hens in the future so that in the future the need for eggs in Indonesia is stable and can meet the demands of the Indonesian people. The method used in this research is a machine learning algorithm, namely Polak-Ribiere which is one of the artificial neural network methods that is often used to predict data. This study does not discuss the prediction results, but will discuss the ability of the Machine Learning algorithm to make predictions based on the egg production dataset of laying hens obtained from the Central Statistics Agency. The research data used is data on the production of laying hens in Sumatra from 2015-2020. Based on this data, network architecture models will be determined, including 4-5-1, 4-10-1, 4-15-1, 4-20-1, and 4-25-1. Of the five models, training and testing were carried out first and then obtained the results that the best architectural model was 4-25-1 with 0.03144841, the lowest among the other 4 models. So it can be concluded that the model can be used to predict the egg production of laying hens.