JCSE, vol. 16, no. 3, pp.153-164, 2022

DOI: http://dx.doi.org/10.5626/JCSE.2022.16.3.153

Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naive Bayes Classifier

Anton Yudhana, Rusydi Umar and Sabarudin Saputra
Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia Master Program of Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

Abstract: Fish is one of the food sources that should be examined for freshness before being consumed. The consumption of rotten fish can cause various diseases. The rotten fish have changed color on the gills, skin, flesh, and eyes and have a pungent odour. Fish freshness can be assessed using a variety of conventional methods, but these methods have limitations, such as requiring relatively expensive equipment, trained personnel and being destructive. The machine learning method is used because it is non-destructive, reduces costs, and is easy to use. This study aims to identify the freshness of fish using k-nearest neighbor (k-NN) and Naive Bayes (NB) classification methods based on the fish-eye image. The features used in the classification process are RGB and GLCM. The research stages consist of the fish collection process, image acquisition and class division, preprocessing and ROI detection, feature extraction and dataset split, and the classification process. Based on these results, it can be stated that the k-NN method has better performance than NB with average accuracy, precision, recall, specificity, and AUC of 0.97, 0.97, 0.97, 0.97, and 0.97.

Keyword: Fish freshness identification; Machine learning; Fisheye images; Computer science; k-NN; Naive Bayes

Full Paper:   235 Downloads, 905 View