Sugiono Sugiono, Rudy Soenoko, Lely Riawati
The purpose of the paper is to investigate the impact of physiological and environmental factors on milk productivity by using artificial intelligence (AI). The model will be useful for the user to decide the best cow treatment in order to gain the best milk production. The research starts with a literature review and an early survey of cattle physiological, environment factors and milk productivity. The next step is measuring the environment data (temperature, wind speed, noise level and relative humidity) and measuring the physiological aspect (heart rate, body temperature) correlated with the milk productivity in 500 pairs of data. All the data are collected and stored into the database and then trained and validated using Back Propagation Neural Network (BPNN) with Genetic Algorithm (GA) optimization. The initial BPNN architectures are selected in 2 hidden layers, delta bar delta learning rule, sigmoid transfer function and epoch 10000. The sensitivity analysis of all independent factors with temperature, relative humidity, core body temperature and heart rate in milk production are successfully presented. Finally, the research successfully increases cow milk production at an average = 0.96 kg/day. © 2017 Praise Worthy Prize S.r.l. - All rights reserved.
The Brawijaya University, Industrial Engineering Dept., Indonesia
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