As a form of innovation in a promotion media Tourism Objects in Indonesia especially at Purbalingga District, one is through Mobile Augmented Reality (MAR). The utilization of technology application of MAR, give the impression of interactive and real towards an object tourism and provide a special experience for tourists to get the information completely including the tourism location. To deliver care facilities to users, we need the evaluation to development or improvement for next application. The method used in this research is evaluation of user satisfaction towards the multimedia elements. The result of MAR user satisfaction showed that almost all respondents are well satisfied.

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This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21-24 June 2013 (192 samples series data) in ICT Unit of Mulawarman University, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.

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