EKSTRAKSI FITUR UNTUK MENGIDENTIFIKASI MARGA TANAMAN MENGGUNAKAN ALGORITMA BACKPROPAGATION - Jatmika, Sunu; Aprilianto, Tria; Idris, Muhammad (P3M Politeknik Negeri Banjarmasin, 2020)
Feature extraction is the beginning to be able to classify and interpret images by linking the characteristics of the leaves into a group of clans according to their type. The algorithm used is backpropagation based on shape and color. The formulation of the problem taken is how the use of the backpropagation algorithm can improve the quality of the community in identifying leaf images. The purpose of this study is to make it easier for the general public to recognize plants, especially the family Azadirachta, Swietenia, and Khaya. This study uses data collection techniques in the form of observation, interviews, and documentation. Data analysis is done by entering content into the system. Data will be input into the learning machine obtained from feature extraction and processed with the backpropagation method. System design uses backpropagation algorithm to classify plants through leaf features. This system uses Android Studio and SQLite databases. The results of this study are that of 9 test data there are 8 recognizable data and 1 incorrectly recognized data. The data shows the accuracy of the backpropagation algorithm in facilitating the general public to recognize plants, especially the family Azadirachta, Swietenia, and Khaya is 88.9%. In addition, the results of the questionnaire show that the backpropagation algorithm has 66% application benefits, 76% ease of interaction, and 80% application display. The overall average of the benefits of each aspect is 74.2%.