(untitled) (Informatica, 2025)
This study presents a machine learning-based method for classifying six freshwater fish species commonly consumed in Indonesia: gourami (gurame), catfish (lele), tilapia (nila), barb (melem), Java barb (tawes), and pomfret (bawal). A total of 132 images, with 22 images per species, were collected from online sources and direct field photography. The classification model utilizes a Naïve Bayes algorithm, employing color feature extraction based on the Hue, Saturation, and Value (HSV) color space. The HSV method decomposes image color into three components—Hue (the color type, such as red, blue, or green), Saturation (the intensity or vividness of the color), and Value (the brightness or lightness of the color)—allowing for improved distinction between morphologically similar species, such as barb and tilapia. Image preprocessing included resizing, background removal, and conversion from RGB to grayscale prior to HSV feature extraction. The dataset was split into training and testing subsets, with 20% of the data allocated for testing. The model’s performance was evaluated using a confusion matrix, and it achieved a classification accuracy of 79.17%. This result surpasses the accuracy reported in comparable studies, such as one on frozen tuna classification, which achieved 72.73% using similar techniques. The findings validate the effectiveness of the Naïve Bayes classifier for species identification tasks in fisheries. Moreover, the approach offers a computationally efficient solution suitable for environments with constrained data availability and limited computational resources. This study underscores the practical value of machine learning in aquaculture, highlighting its potential for enhancing species monitoring, quality control, and automated recognition using relatively small datasets.