Freshness Classification of Milkfish Using the Naà ̄ve Bayes Algorithm
| ctrlnum |
24496 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>http://repository.unika.ac.id/24496/</relation><title>Freshness Classification of Milkfish Using the Naïve Bayes Algorithm</title><creator>Priyono, Christianto Kurniawan</creator><subject>000 Computer Science, Information and General Works</subject><description>Ikan bandeng is a great demand for food as well as a livelihood for fishermen and traders. There are several ways that used as a manual alternative to test the freshness level of fish.
In this research, a system was built to detect the ikan bandeng's freshness level with Naive Bayes Classifier method. This research uses 150 training data
and 48 testing data that the training data is one of the most important factor. Based on the trial's result with one parameter, the result obtains 72,91% of
accuracy. Meanwhile the second trial is using 100 training data that obtains 75% of accuracy, and the third trial is using 150 training data that obtains 79,12% of
accuracy. The research uses three parameters gain 93,75% of accuracy.
The factors that influence the algorithm are mean value (R,G,B), the value of deviation standart on training data, and the amount of training data.</description><date>2020</date><type>Thesis:Thesis</type><type>PeerReview:NonPeerReviewed</type><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/1/16.K1.0042_COVER.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/2/16.K1.0042_BAB%201.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/3/16.K1.0042_BAB%202.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/4/16.K1.0042_BAB%203.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/5/16.K1.0042_BAB%204.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/6/16.K1.0042_BAB%205.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/7/16.K1.0042_BAB%206.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/8/16.K1.0042_DAFTAR%20PUSTAKA.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.unika.ac.id/24496/9/16.K1.0042_LAMPIRAN.pdf</identifier><identifier> Priyono, Christianto Kurniawan (2020) Freshness Classification of Milkfish Using the Naïve Bayes Algorithm. Other thesis, Universitas Katolik Soegijapranata Semarang. </identifier><recordID>24496</recordID></dc>
|
| language |
eng |
| format |
Thesis:Thesis Thesis PeerReview:NonPeerReviewed PeerReview Book:Book Book |
| author |
Priyono, Christianto Kurniawan |
| title |
Freshness Classification of Milkfish Using the Naà ̄ve Bayes Algorithm |
| publishDate |
2020 |
| topic |
000 Computer Science Information and General Works |
| url |
http://repository.unika.ac.id/24496/1/16.K1.0042_COVER.pdf http://repository.unika.ac.id/24496/2/16.K1.0042_BAB%201.pdf http://repository.unika.ac.id/24496/3/16.K1.0042_BAB%202.pdf http://repository.unika.ac.id/24496/4/16.K1.0042_BAB%203.pdf http://repository.unika.ac.id/24496/5/16.K1.0042_BAB%204.pdf http://repository.unika.ac.id/24496/6/16.K1.0042_BAB%205.pdf http://repository.unika.ac.id/24496/7/16.K1.0042_BAB%206.pdf http://repository.unika.ac.id/24496/8/16.K1.0042_DAFTAR%20PUSTAKA.pdf http://repository.unika.ac.id/24496/9/16.K1.0042_LAMPIRAN.pdf http://repository.unika.ac.id/24496/ |
| contents |
Ikan bandeng is a great demand for food as well as a livelihood for fishermen and traders. There are several ways that used as a manual alternative to test the freshness level of fish.
In this research, a system was built to detect the ikan bandeng's freshness level with Naive Bayes Classifier method. This research uses 150 training data
and 48 testing data that the training data is one of the most important factor. Based on the trial's result with one parameter, the result obtains 72,91% of
accuracy. Meanwhile the second trial is using 100 training data that obtains 75% of accuracy, and the third trial is using 150 training data that obtains 79,12% of
accuracy. The research uses three parameters gain 93,75% of accuracy.
The factors that influence the algorithm are mean value (R,G,B), the value of deviation standart on training data, and the amount of training data. |
| id |
IOS2679.24496 |
| institution |
Universitas Katolik Soegijapranata |
| institution_id |
334 |
| institution_type |
library:university library |
| library |
Perpustakaan Universitas Katolik Soegijapranata |
| library_id |
522 |
| collection |
Unika Repository |
| repository_id |
2679 |
| subject_area |
Akuntansi Arsitektur Ekonomi |
| city |
SEMARANG |
| province |
JAWA TENGAH |
| repoId |
IOS2679 |
| first_indexed |
2023-02-24T11:09:41Z |
| last_indexed |
2023-02-24T11:09:41Z |
| recordtype |
dc |
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1765771994335281152 |
| score |
17.538404 |