LAND COVER SPECTRAL DATA CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK (ANN)

Febri Maspiyanti (Informatics Engineering, Faculty of Engineering, Pancasila University)
Ionia Veritawati (Informatics Engineering, Faculty of Engineering, Pancasila University)
Amir Murtako (Informatics Engineering, Faculty of Engineering, Pancasila University)
Riadika Mastra (Informatics Engineering, Faculty of Engineering, Pancasila University)
Jullend Gatc (Information Systems, Kalbis Institute)

Jurnal TAM (Technology Acceptance Model)
Vol 13, No 2 (2022): Jurnal TAM (Technology Acceptance Model)

Bamboo is one of the plants in the tropics which is also a lignocellulosic natural material which can be used as a substitute for wood. There are hundreds of types of bamboo in Indonesia, where each type has its own characteristics in its use so that bamboo has potential in the industrial sector where when combined with innovation and creativity it has high economic value and is in demand by domestic and foreign consumers. The use of "Remote Sensing" technology, especially in terms of identifying bamboo and vegetation and other objects, has been proven through research related to land cover classification. This study aims to classify land cover spectrum data using an Artificial Neural Network algorithm.Classification of 36 data consisting of light bamboo, dark bamboo, dry soil, wet soil, bricks, concrete, grass, and taro leaves was evaluated using accuracy techniques. The resulting accuracy is 94.45%.