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Food is one of the primary needs that is needed by humans throughout their life. Every day humans need to eat to meet the nutritional needs of the body so that they can do their activities properly. That is why changes in food prices are always a concern for everyone. The fluctuation of food prices can be a determinant for everyone to choose what food they will consume, according to their financial condition. This study aims to forecast food prices in the future by using data on food prices in the past. With price forecasting, it can be useful for planning expenditures such as monthly shopping planning and determining the selling price of food. Price changes always occur following the influencing factors, such as changes in weather and an increase in the demand for several foodstuffs on major holidays. The method used in this research is the Holt-Winters Exponential Smoothing Method. This method is a method of forecasting, which in addition to paying attention to trend factors, also observes season factors (seasonal). This study only uses food prices in Pamekasan Regency. The data used is data on food prices for the period 2012-2019. The next forecast is carried out in the following year, namely 2020. The results show that forecasting using the Holt-Winters Exponential Smoothing Method has a good accuracy value with an average MAPE value of 1.2% for the Multiplicative Model and 1.02% for the Additive Model. This result shows that Additive Model is better than Multiplicative Model because it has a smaller MAPE value.
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Stored only when both papers are in the corpus — 28 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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