<![CDATA[Inflation forecasting is complicated. Inflation rate calculated based on the rise in the consumer price index (CPI) is influenced by various factors ranging from volatile prices of various types of goods, rupiah exchange rate, world inflation rate, government policy, fluctuations in the supply of goods and demand. Hybridation algorithm of support vector regression (SVR) with chaotic sequences and genetic algorithms has been successfully applied to improve the accuracy of forecasting in various fields. But it has not been explored the usability of this algorithm in the field of market economy which is forecasting inflation. This journal will analyze the potential of hybridization algorithm that which is chaotic genetic algorithm-simulated annealing algorithm (CGASA) with SVR model to improve the performance of forecasting accuracy. With the chaotic sequence of chaotic sequences, it will be able to avoid premature local optimum and early convergention, especially with the simulated annealing algorithm that increases the search area of ​​the solution. The results of the forecasting test in this study show better accuracy than the previous research which has been studied is the combined ensemble method between autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) algorithm.]]>
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