| dc.description.abstract | Humidity prediction is not only the key success factor in maintaining product quality
and shelf life but also an opportunity to save energy and steam in drying technology.
The purpose of this thesis is to determine the predictive effectiveness of machine
learning techniques in the coffee manufacturing process.
The beverage company that specialized in coffee drying techniques provided the
practical data for this thesis. Although they have invested a lot in their database, it has
not yet been fully utilized in the investigation of the relationship between process
parameters and moisture prediction. There are two supervised machine learning
techniques (ANN and ANFIS) are conducted, two transformation data techniques
(Normalization and Standardization), two evaluation measures (MAE and RMSE), and
two feature selection techniques (Mutual Information and Correlation). As the result,
the ANFIS method shows an outweigh performance in predictive food drying
techniques, especially coffee products.in | en_US |
