Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method

Autor: Suwarsito Suwarsito, Hindayati Mustafidah, Tito Pinandita, Purnomo Purnomo
Jazyk: indonéština
Rok vydání: 2022
Předmět:
Zdroj: Jurnal Informatika, Vol 10, Iss 2, Pp 183-189 (2022)
Druh dokumentu: article
ISSN: 2086-9398
2579-8901
DOI: 10.30595/juita.v10i2.15471
Popis: Indonesia is a maritime and agricultural country with enormous world fishery potential. The large variety of fish is often confusing for ordinary people in recognizing types of fish, especially freshwater fish. It was stated that the types of freshwater fish often consumed by the Indonesian people are bawal (pomfret), betutu, gabus (cork), gurame (carp), mas (goldfish), lele (catfish), mujaer (tilapia), patin (asian catfish), tawes, and nila (tilapia nilotica). Some fish types have similar shapes, so it is tricky to tell them apart. Meanwhile, in the digitalization era today, Artificial Intelligence (AI)-based technology has become a demand in all areas of life. It is overgrowing, not apart from the fisheries sector. Therefore, in this study, the K-Nearest Neighbor (KNN) method was applied as one of the methods in AI to identify and classify freshwater fish species based on their images. The KNN method classifies new data into specific classes based on the distance between the new data and the closest k data through the learning process. This KNN model is built by preparing the dataset stages, separating the dataset into data-train and data-test with a ratio of 70%:30%, then building and testing the model. The dataset is freshwater fish images, totaling 100 images from 10 freshwater fish types. Model testing is done by measuring performance using a confusion matrix. Based on the test results, the model has an accuracy performance of 70%. Thus, KNN can be used as a model to identify freshwater fish species based on their image.
Databáze: Directory of Open Access Journals
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