Classification of Orchid Flowers Using the Decision Tree Algorithm

Abstract

Orchids are one of the ornamental plants that have various species with different characteristics, making them difficult to identify manually by the general public. Therefore, a system is needed that can classify orchid species automatically and accurately. This study aims to develop a classification system for orchid species using the Decision Tree algorithm. The method used in this study includes collecting a dataset of orchid flower images, data preprocessing, labeling, and extracting relevant features. Furthermore, the data is used for the training process using the Decision Tree algorithm to produce a classification model. The resulting model is then tested using test data to determine the accuracy level in classifying orchid species. The results show that the Decision Tree algorithm is able to classify orchid species with an accuracy ranging from 85% to 92%, which is categorized as good. This indicates that the method used is quite effective in recognizing and distinguishing orchid species based on image characteristics. This system is expected to help users identify orchid species more easily, quickly, and efficiently.

Keywords: Classification, Orchid, Decision Tree, Machine Learning, Image Processing, Camera