The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

Feroza Rosalina Devi, Endang Sugiharti, Riza Arifudin

Abstract


The beef cattle quality certainly affects the quality of meat to be consumed. This research
performs data processing to do the classification of beef cattle quality. The data used are
196 data record taken from data in 2016 and 2017. The data have 3 variables for
determining the quality of beef cattle in Semarang regency namely age (month), Weight
(Kg), and Body Condition Score (BCS) . In this research, used the combination of Naïve
Bayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification and
K-Means. After doing the combinations, then conducted analysis of the results of which
type of combination that has a high accuracy. The results of this research indicate that the
accuracy of combination Naïve Bayes Classification and K-Means has a higher accuracy
than the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seen
from the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifier
of 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithm
is 98,33%, so it can be concluded that combination of K Means Clustering algorithm and
Naïve Bayes Classifier is more recommended for determining the quality of beef cattle in
Semarang regency.

Keywords


Beef Cattle Quality, Combination, Naïve Bayes Classification, Fuzzy CMeans, K-Means

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DOI: https://doi.org/10.15294/sji.v5i2.15452

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