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

Zilvanhisna Emka Fitri (Politeknik Negeri Jember)
Wildan Bakti Nugroho (Politeknik Negeri Jember)
Abdul Madjid (Politeknik Negeri Jember)
Arizal Mujibtamala Nanda Imron (Universitas Jember)

Jurnal Rekayasa elektrika
Vol 17, No 2 (2021)

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is 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 pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.