Analysis of the Naïve Bayes Method in Classifying Formalized Fish Images Using GLCM Feature Extraction

Ayu Pariyandani (Universitas Medan Area)
Eka Pirdia Wanti (Universitas Medan Area)
Muhathir Muhathir (Universitas Medan Area)

Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Vol 1, No 2 (2020)

Fish is one of the foods that are high in protein so that many Indonesians consume fish as protein intake for health. Fish can be found in any waters including Indonesian marine waters, so that some of the Indonesian people work as fishermen. This causes the number of fish catches to increase and the fishermen have to sell the fish quickly in at least one day because the fish will rot easily if not consumed immediately. This has led some traders to cheat by mixing formaldehyde with fish that are not sold out. This action is very detrimental to consumers, so they must be more vigilant in choosing or buying fish on the market. One way for consumers to recognize formaldehyde fish is a technology that can distinguish fresh fish or formalin fish based on the image of the fish, Naive Bayes and GLCM (Gray Level Co-Occurrence Matrix) by using this method the accuracy of this system can reach up to 70%.