Decision Support System for Selection of Staples Food and Food Commodity Price Prediction Post-COVID-19 Using Simple Additive Weighting and Multiple Linear Regression Methods

Abstract

In the pandemic after the occurrence of COVID-19, there are significant changes in economic statistics, this is influenced by economic activity that is not stable compared to before. The price of food staples was also affected by the pandemic, meetings between buyers and traders, usually held in traditional and modern markets, were hampered due to government restrictions on the territory. This causes a decrease in existing transactions in the market, therefore foodstuffs have the possibility of price volatility. Multiple Linear Regression (MLR) algorithm is a method that can overcome predictions with the type of seasonal dataset prediction, therefore the MLR algorithm is implemented to predict food prices, especially in the modern market, based on the predicted prices, then a decision support system is made to make an alternative ranking of food selection accumulation. Based on the available food ingredients there are nutrients contained in these foods, therefore experts are needed to determine the weighting of nutrition in each food ingredient. Simple Additive Weighting (SAW) method is a method that can do weighting and ranking of alternatives. Therefore the SAW method is applied to rank alternative food staples that have nutritional weight and price. Based on the application of MLR, the error level testing concluded that the prediction of the price of food “Rice” has the least error results compared to other foodstuffs with the value of MSE 21261.04, MAE 145.79, RMSE 145.812, MAPE 0.81 while for the best R2 values found at food ingredients “Garlic” with a value of 0.576. Based on testing of the application of SAW, the same results are obtained between manual calculations and calculations provided by the system, so that the accuracy of the system can be ascertained.