Higher education in the future is faced with competition that emphasizes the role, innovation and use of information and communication technology, integration between universities and society, industry and corporations cannot be ignored anymore. Learning innovation is a must for universities. The Global Sector that has an impact on the internal and external elements of the university. The purpose of making this learning framework is to give an overview, reference and assist higher education in facing the impact of the 4.0 higher industrial revolution in which the role of information technology, regulations, facilities and infrastructure, budget and university management are the basic capital that must be fulfilled. Besides that online learning, virtual learning, social learning, open learning and blended learning become a necessity and necessity in the era of the industrial revolution where adjusting learning materials and curriculum with industry and corporations is highly recommended. With the hope of graduates having soft skills, ethics and critical thinking, cultured and global minded and innovative.

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Gross Domestic Product (GDP) is a benchmark for economic production conditions of a country. Estimates of economic growth in the coming year in a country has important roles, among others as a benchmark in determining business plans for business entities, and the basis for devising government fiscal policy. Artificial Neural Network (ANN) has been increasingly recognized as a good forecasting tool in various fields. Its nature that can mimic the workings of the human brain makes it flexible for non-linear and nonparametric data. GDP growth forecasting techniques using ANN has been widely used in various countries, such as the United States, Canada, Germany, Austria, Iran, China, Japan and others. In Indonesia, forecasting of GDP is only done by government institutions, namely National Planning Board, using macroeconomic model. In this study, ANN is used as a tool for forecasting GDP growth in Indonesia, using some variables, such as GDP growth in the two previous periods, population growth rate, inflation, exchange rate and political stability and security conditions in Indonesia. Results from this study indicate that ANN forecasts GDP relatively better than the one issued by the government. Further study would be to use ANN to predict other economic indicators.

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