Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca)

Fitri, Zilvanhisna Emka and Nugroho, Wildan Bakti and Madjid, Abdul and Imron, Arizal Mujibtamala Nanda (2021) Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca). Jurnal Rekayasa Elektrika, 17 (2). pp. 123-128. ISSN 1412-4785

Text (Hasil Similarity) Similarity_Comparison of Neural Network Methods for Classification of Banana Varieties (Musa Paradiasaca).pdf - Supplemental Material Available under License Creative Commons Attribution Share Alike. Download (1MB)

Abstract

Every region in Indonesia has a massive diversity of banana species, but no system records information about the characteristics of banana varieties. This research aims to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods. The varieties of bananas used are Merah bananas, Mas Kirana bananas, Klutuk bananas, Raja bananas and Cavendis bananas. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and fruit length). The intelligent system used is the backpropagation method and the radial basis function neural network. The results showed that both approaches could classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the radial basis function neural network.

Actions (login required)

View Item