<![CDATA[This study aims to determine the Value at Risk (VaR) analysis process in measuring the maximum level of losses that can be accepted in the investment process to acquire assets that are already operating. An investment program must be able to measure the risks that will be faced in the future so that the VaR analysis. This study uses a quantitative approach through secondary data. The techniques used are data preprocessing scaling techniques, correlation between features and forecasting modeling through linear and non-linear regression mechanisms through repeated simulations (Monte Carlo). The research method for this VaR analysis uses several features from historical data with a probability level of 95%. From the results of the simulation of VaR, a prediction was obtained for the next 3 years, investors will not suffer losses, so that the profit obtained is $ 25 million from the estimated asset return value of 1.3% with a Mean Squared Error (MSE) value of 0.13. Based on the weighting results, it was also found that the asset value volatility parameter has the largest weight that affects the VaR value.]]>
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