The Prediction of Subsidized Fertilizer Stock Using Least Square Support Vector Machine on The Kartu Petani Berjaya Aplication

Agriculture is one of the biggest commodities in Lampung, so that this also causes a lot of use and allocation of subsidized fertilizers. In terms of this it is very important to know how much amount of subsidized fertilizer needed in the future to prepare subsidized fertilizer stocks. The data needed was the time series data from subsidized fertilizer redemption data, using Least Square Support Machine and Autoregressive Integrated Moving Average methods to make a prediction model for subsidized fertilizer redemption. The result was hoped that we can find out how many harvests are in Lampung and the future subsidized fertilizer rations. This research was expected to provide benefits to the relevant parties.