Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN
Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of fr...
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2023-11-01
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| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1740 |
| _version_ | 1797280251503443968 |
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| author | Regina Vannya Arief Hermawan |
| author_facet | Regina Vannya Arief Hermawan |
| author_sort | Regina Vannya |
| collection | DOAJ |
| description | Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm. |
| first_indexed | 2024-03-07T16:37:30Z |
| format | Article |
| id | doaj.art-7d606fc7d2d64d3387b04517755540c7 |
| institution | Directory Open Access Journal |
| issn | 2301-7988 2581-0588 |
| language | English |
| last_indexed | 2024-03-07T16:37:30Z |
| publishDate | 2023-11-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj.art-7d606fc7d2d64d3387b04517755540c72024-03-03T09:34:26ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882023-11-0112339440010.32736/sisfokom.v12i3.1740816Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NNRegina Vannya0Arief Hermawan1Program Studi Informatika, Fakultas Sains & TeknologiProgram Studi Informatika, Fakultas Sains & TeknologiChicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1740chicken classificationfreshness levelcolor extractionnaïve bayes classifierhistogram |
| spellingShingle | Regina Vannya Arief Hermawan Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN Jurnal Sisfokom chicken classification freshness level color extraction naïve bayes classifier histogram |
| title | Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN |
| title_full | Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN |
| title_fullStr | Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN |
| title_full_unstemmed | Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN |
| title_short | Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN |
| title_sort | performance analysis of chicken freshness classification using naive bayes decision tree and k nn |
| topic | chicken classification freshness level color extraction naïve bayes classifier histogram |
| url | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1740 |
| work_keys_str_mv | AT reginavannya performanceanalysisofchickenfreshnessclassificationusingnaivebayesdecisiontreeandknn AT ariefhermawan performanceanalysisofchickenfreshnessclassificationusingnaivebayesdecisiontreeandknn |