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"In November 2008, the Regional Economic Studies Programme of the Institute of Southeast Asian Studies (ISEAS) and the Singapore office of the International Development Research Centre (IDRC) of Canada organised a forum on Regional Economic Integration ASEAN and Canadian Perspectives."

"ABSTRAKThis study aimed to analyze the competitiveness of Indonesian fishery products in the ASEAN and Canada markets. The method used was Revealed Comparative Advantage (RCA), Export Product Dynamic (EPD), and X-Model product export potential. The research showed that Vietnam and Canada had a similar level of export structure to Indonesia in the ASEAN market so that Indonesia would have a high competition with both countries. Indonesian fishery products showed a high competitiveness in the export destination markets, except Philippines and Canada. The market position of Indonesian fishery products in Philippines, Thailand and Canadian markets was in the rising star and lost opportunity. These countries also showed as an optimist and potential market for Indonesian fishery products. The policy implication is that government and private sector need to prioritize the export of fishery products to Thailand, Philippines and Canada by improving the product competitiveness through quality improvement and production cost efficiency."

"ABSTRAK Meningkatnya perhatian yang diberikan oleh para pakar, baik melalui berbagai studi empiris maupun publikasi teoritis, terhadap pentingnya membaca termasuk bahasa Inggris, telah menimbulkan pertanyaan apakah mungkin membaca pemahaman disekolah menengah dapat dipengaruhi oleh aspek lain. "

"This study was aimed to determine maturity and ripeness of sawo (Achras sapota L.) based on near infrared (NIR) spectroscopy using artificial neural network. The NIR system was developed and applied to 120 sawo samples at the wavelength range from 1400 - 1995 nm, the data was recorded in 5 nm interval. The samples were separated into three group, i.e. mature, ripe, and over ripe based on their harvest time. The principal component analysis (PCA) was used to reduce dimension of NIR reflectance data that has been smoothed with moving average method. The 5, 10, 15 principal component was fed into the neural network model as input and the level of maturity and ripeness as output. The result recommended the use of 10 and 15 principal component as input on various nodes in hidden layer that would provided the highest accurateness of 100% in classifying the sawo based on its maturity and ripeness"