KLASIFIKASI KESUBURAN TANAH DENGAN METODE PRINCIPAL COMPONENT REGRESSION - Calvin Calvin; Janson Hendryli (Jurnal Ilmu Komputer dan Sistem Informasi, 2016)
Land be a pleasant to plant life and a layer of earth most outer derived from weathering rocks a have the depth and its own characteristic and organic matter land was the result of weathering plant remains and the animal that mingled with mineral matter another out of the ground at the top layer of soil. The aim of this research is to predict the soil fertility. In this research, the data training and testing is from Landsat 7 Image in Jakarta and Bogor with 6 bands. There are two steps in doing the experiment, training and testing. We use Principal Component Regression in training to find the best model that will be used in testing and return it in the form images in testing. For the evaluation we used Analysis of Variance. Principal Component Regression got 37,35% average error in Jakarta and 35,8% average error in Bogor.