Identification of the quality of premium and non-premium rice based on physical characteristics using artificial neural networks and digital image processing

Counterfeiting of quality rice was rife in Indonesia. This research was conducted to develop technology to identify differences in premium and non-premium rice quality based on pre-existing digital images. Artificial neural networks and digital image processing methods to identify premium and medium (non-premium) rice quality were applied in this research. Statistical analysis of this study used the SPSS program. This research is observation-type research. This research design uses an artificial neural network with uses 3 layers, namely the results of shape feature extraction on the metric, eccentricity, area, and perimeter parameters as input or input layers, hidden or hidden layers, and premium rice and non-premium (medium) rice as output or output layers. This research uses 52 images as training and 20 images as testing. The obtained image was taken at a distance of 25 cm. This research showed that the results of training using artificial neural networks (ANN) on 52 images obtained an accuracy of 92%. The test results using 20 images obtained 95% accuracy, 63.33% sensitivity, and 10% specificity. Based on statistical analysis using the Mann-Whitney test, it obtained the asymph value. Sig (2-tailed) < 0.05 indicates the difference between premium and non-premium rice using metric, eccentricity, perimeter, and area parameters.