A Machine Learning Approach for Coconut Sugar Quality Assessment and Prediction
Abstractâ?? This study presents a machine learning approach to
accurately assess the quality of coconut sugar using RGB values.
Python and scikit-learn were used to run the following machine
learning algorithms: artificial neural network (ANN), stochastic
gradient descent (SGD), k-nearest neighbors (k-NN) algorithm,
support vector machine (SVM), decision tree (DT) and random
forest (RF). Comparisons were made between the aforementioned
machine learning algorithms by evaluating the accuracy and the
average running time of each training model. Results of the study
show that the SGD is superior in terms of accuracy but falls short
to k-NN and SVC in terms of running time. In this fashion, a plot
between the accuracy and the running time was made and it was
observed that algorithms with higher accuracies correspondingly
have also higher running times. By this very nature, experimental
results show that the SGD holds merit in accurately assessing the
coconut sugar quality, despite its expense in running time.
KeyWords
coconut sugar, machine learning, prediction,
quality assessment
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