Rice is an agricultural commodity that is a staple food in Indonesia with hundreds of types of rice that have different characteristics. The type of rice can be distinguished from color and shape. The main feature that is dominant and can distinguish each type of rice is the color and shape. This feature is the main key in identifying types of rice. Identification is done by comparing the similarity of rice images using the value of color and shape features. The similarity can be determined through the difference in feature values between the query image and the database image. The closer the difference is to zero, the higher the level of similarity. The degree of similarity will affect the accuracy of image recognition at the time of identification. In this study, an analysis of the accuracy of image identification and measurement of computation time was carried out. Improved identification accuracy using the weighting of color and shape feature values. Extraction of the two value features using the invariant moment and color moment. Preprocessing before extraction using Grayscale, resize, edge Enhancement, Histogram Equalization. Clustering of rice image data using K-Means clustering. The results showed that the accuracy of identification with 400 rice image test data, reached more than 95% in the weighting scheme Ws (weighted Shape) = 40% and Wc (weighted color) = 60% with an average computing time of 5 milliseconds at 10 the cluster.
Uncontrolled erosion would cause considerable damages, such as soil fertility decline, water structures damage and reservoirs sedimentation. As the data for the sedimentation rate are limited, several models have been developed to predict the surface erosion and the rate of sedimentation. However, the availability of sufficient, diverse and extensive data is needed for the implementation of the models, both for the model calibration and the verification. The result of the analysis shows that both of the Water Tank Models that represent the erosion-sedimentation rate process, in which Water Tank 1 being the three-tank cascade system and Water Tank 2 being two-tank cascade system, are not optimum. This can be observed from the values of volume error (VE), relative error (RE), root-mean-square error (RMSE) and correlation coefficient (R) that show the effect of 1.5 hours of rain period in the sedimentation rate. The field condition shows considerable sedimentation, on the other hand, the models’ simulations show decreasing sedimentation rates. The optimum model’s parameters for Water Tank 1 and Water Tank 2 are 924.51%-1049.26% for the relative error, 50.81% - 121.42% for the volume error, 0.9 for the correlation coefficient and 6703.59-17,297.85 for the root-mean-square error. The parameters and constant’s values of the models are different relative to the drainage basins’ condition.
The image has the features of shape, color and texture that are vary. Each feature has a different performance in supporting the accuracy of information retrieval using a process approach to CBIR (Content-Based Image Retrieval). On the image with different objects different performance will be generated on each feature. For example, that the performance features of the form of the more dominant compared features color and texture on the image with the face, while the object on the image with the object of interest feature is more dominant than the features of texture and shape. In this research was conducted on the analysis of the performance features of the shape, color and texture in supporting the accuracy of a search using the approach of CBIR (Content Based Image Retrieval). The method used are invariant moment, color moment and GLCM (Grey Level Co-occurrence Matrix). The results showed that the best search accuracy is 95%, where the features of shape has a performance by 50%, 30% color feature and texture feature by 20% with 600test image with object database face.
Environmental degradation as a result of deforestation carried out in the Catchment resulted in a decrease in its ability to store water. This has the effect of increasing the amount of sediment discharge. The process of estimating sediment discharges is very difficult because the data input variables are many and varied, usually, the data are very limited, because the erosion process occurs until the sediment discharge mechanism is quite complex. The process of sediment discharges in Catchment s is influenced by rain and surface runoff and is represented in the storage type. In this study, an approach using the Tank Model was conducted. The purpose of this study is to develop a tank model for sediment discharge analysis in disaster mitigation. The steps are setting the field experiment for collecting rain and discharge sediment data as the model input and setting the model analysis by making the structure and formulation of the tank model. There are 3 proposed tank models namely Tank Model 1 (three tanks, series, and cascade), Tank Model 2 (two cascade tanks), and Tank Model 3 (three cascade tanks). Model parameters are determined using the Genetic Algorithm (AG) method optimization approach. The analysis shows that Tank Model 3, composed of 3 (three) cascade tanks, represents a Catchment better than the other 2 tank models. This can be seen from the value of the accuracy of the model, namely the value of volume error (VE), the value of relative error (RE), the value of the mean least square error (RMSE), and the value of the correlation coefficient (R). But still has a range of differences for the value of sediment discharges, the cause may be a factor in the pattern of rain spread in the hydrological process, synchronization of the measurement process and data length, and the possible assumptions of the model parameters.
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