Judul: Development of machine for evaluating quality of rice by using real time image processing
Abstrak: Quality assessment of rice prior to marketing is very important. Up to now, the rice quality inspection is conducted visually by trained examiners who have expertise and experience, but the method used has disadvantages such as: (1) the subjectivity factor that causes rice quality testing results to be biased between the observer, (2) the physical exhaustion of observer causes the observation result is inconsistent, and (3) the time required relatively much longer. The purpose of this study was to develop a method of determining the physical quality of rice by image processing techniques in real-time. The method used was the technology of image processing and artificial neural networks. Architecture of ANN to predict the physical quality of rice was built with 13 input layers, 20 hidden layers and 8 output layers, while the ANN to predict the degree of rice milling was built with 9 input layers, 20 hidden layers and 5 output layers. The results showed that the machine for testing rice physical quality in real time had worked functionally as expected, both hardware and software. The training process of the 5 rice varieties showed good results, especially in the estimation of head rice which was above the average 90 %, whereas the accuracy of validation decreased due to predictive accuracy of foreign matter dropped drastically due to the irregular shape and varied colors, making it difficult for the system to recognize it. The test results show rice milling training and validation results are not consistent, so it can not be resumed in the application process.
Keyword: Rice, quality, real-time image processing, artificial neural network

Judul: Development of grading system based on image processing and artificial neural network for green coffee sorting equipment with belt konveyor type
Abstrak: The objective of this research was to develop a grading system which consisted of a computer program of image processing and artificial neural network to identify quality of green coffee namely A, B, C, and RJ (reject). Two images of the green coffee from top and bottom captured by two cameras were analyzed to get six quality parameters which matched the green coffee quality criteria namely length, area, perimeter, defect area, index of red color, and index of green color. Those six quality parameters were used as training data inputs (75% - 768 kernel) of the developed artificial neural network (ANN). Eighteen variations of ANN were developed for ANN training purposes. The weight of the selected ANN architecture were used to identify the quality class of testing data (25%), and then integrated with image processing program so the program could identify green coffee quality class automatically. The total accuracy of ANN was 67% from top camera with A 59%, B 53%, C 70%, RJ 84%, and the total accuracy was 71% from bottom camera with A 75%, B 45%, C 73%, and RJ 92% based from 256 testing data. The total accuracy of ANN from combination of both cameras was 68%. New training was developed in order to increase prediction accuracy by modified ANN inputs. Prediction accuracy produced by the new ANN weight were A 78%, B 53%, C 70%, RJ 98%, and the total accuracy was 75%
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Judul: Koksidiosis pada Sapi Disebabkan Eimeria Zuernii (Rivolta,1887)
Abstrak: Koksidia merupakan parasit intraseluler yang menyerang traktus digestiyus terutama bagian USUS. Penyebab penyakit ini adalah dari genus Eimeria. Koksidia yang paling patogen terhadap ternak sapi adalah Eimeria Ziuernii (Rivolta, 1887 dalam Todd dan Ernst, 1977). Siklus hidup Eimeria zuernii terdiri dari fase eksogen yang disebut juga dengan tahap sporogoni dan fase endogen yang terdiri dari tahap skizogoni dan tahap gametogoni (Soulsby, 1982).
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