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

• Main Authors: Feroza Rosalina Devi, Endang Sugiharti, Riza Arifudin
• Format: Article
• Language: English
• Published: Jurusan Ilmu Komputer Universitas Negeri Semarang 2018-11-01
• Series: Scientific Journal of Informatics
• Subjects: beef cattle quality, combination, naïve bayes classification, fuzzy cmeans, k-means
• Online Access: https://journal.unnes.ac.id/nju/index.php/sji/article/view/15452

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.