Implementation of ARIMA Method in Short-Term Prediction Accuracy of Basic Needs Prices in Cirebon City
Keywords:
ARIMA, Basic Commodity Price Prediction, Cirebon City, Prediction Accuracy, MAPEAbstract
The increasing demand for staple goods each year leads to changes in their prices, necessitating predictions related to staple prices to anticipate price hikes, maintain price stability, and prevent commodity shortages. The Autoregressive Integrated Moving Average (ARIMA) method is one of the prediction methods that can be used, but further testing is needed to prove the accuracy of the prediction results produced by the ARIMA method itself. The research focuses on essential food needs in the household sector, including rice, granulated sugar, cooking oil, wheat flour, beef, chicken, eggs, red chili, shallots, and garlic. ARIMA testing is conducted to evaluate the accuracy of short-term price predictions for three months, using price data of staple goods from Cirebon City from 2021 to 2023 and comparing it with 2024 data obtained from the Department of Cooperatives, Small and Medium Enterprises, Trade, and Industry (DKUKMPP) of Cirebon City. Data processing is carried out using Minitab software through four ARIMA stages: model identification, parameter estimation, model diagnostics, and forecasting. The study results in seven high-accuracy ARIMA models, with a Mean Absolute Percentage Error (MAPE) value of <10%, categorizing them as "Very Good." Price predictions for staple goods from January 2024 to March 2024 show an accuracy level of 98.864%. The accuracy of the predictions is influenced by the conditions of each commodity in the market, such as extreme weather, rising feed prices, global market prices, and other factors affecting production, leading to limited commodity stocks in the market and affecting actual price increases.
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