Klasifikasi Gambar Gigitan Ular Menggunakan Regionprops dan Algoritma Decision Tree
DOI:
https://doi.org/10.30865/json.v1i2.1789Abstract
Indonesia has a high biodiversity of snakes. Snake species that exist throughout Indonesia, consisting of venomous and non-venomous snakes. One of the dangers that can be posed by snakes is the bite of several types of deadly snakes. Snake bite cases recorded in Indonesia are quite high with not a few fatalities. Most of the deaths caused by snakebite occur due to errors in the handling procedure for the bite wound. This problem can be overcome one of them if we know how to classify snake bite wounds, whether venomous or non-venomous. In this study, a classification system for snake bite wound image was built using Regionprops feature extraction and Decision Tree algorithm. Snake bite images are classified as either venomous or non-venomous without knowing the kind of the snake. In Regionprops several features are used to help the process of feature extraction, including the number of centroids, area, distance, and eccentricity. Evaluation of the model that was built was found that the parameters of the number of centroids and the distance between centroids had the most significant influence in helping the classification of images of snakebite wounds with an accuracy of 97.14%, precision 92.85%, recall 91.42%, and F1 score 92.06%.
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