Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos - Fadlisyah Fadlisyah; Muhathir Muhathir (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2023)
Fish is one of the foods that are often consumed by humans to complete the protein in the body. Indonesia is rich in animal protein so some rogue traders to avoid losses due to rotten fish, sellers process fish to make it look fresh by fishing with formalin liquids so that buyers think the fish is still fresh, this problem is often found in the market so that it takes the right solution. The solution offered is to apply Machine Learning (Boosting Algorithm) to classify fresh fish and fish that have been planned with formalin by utilizing the extraction of GLCM features. The findings of this study indicate a variation of boosting algorithms can provide solutions to this problem. Accuracy, precision, recall, f1-score, f2-score, and Jaccard score in tilapia fish with a value of 0.95, 0.95, 0.95, 0.95, 0.95, 0.95 this result is obtained by extreme gradient boosting variations with the highest achievements compared to variations Other things are also similar to Tamban fish with a value of 0.78, 0.775, 0.78, 0.825, 0.77, 0.632. The effect of boosting algorithm on the measurement of model performance looks very increased in tilapia fish while in tamban fish do not provide maximum results, but these findings are better