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Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

The aim of this paper was to determine the effect of public sector expenditure to economic growth and poverty in all provinces in Indonesia during period 2006 to 2008. Using Full Information Maximum Likelihood (FIML) approach, the result showed that public sector expenditure on education and health sector had significant effect inboosting economic growth. In addition, the output produced by the education and health sector, both of the output had significant effect on economic growth. While, public sector expenditure on infrastructure had insignificant effect. Furthermore, this study showed that public sector expenditure on education and health also had significant effect in reducing the number of poor through the outcome such as form of school enrollment, literacy, infant mortality and life expectancy. On the other hand, the effect of economic growth to reduce the number of poor was not significant.