Measurement of Detection Rate Accuracy in Forecasting Crude Palm Oil Production using Fuzzy Time Series

Abstract

Time Series is a superior method of predicting the future based on past data. Time series are also used in various businesses to make forecasts for profit. Time series data provide data visualization with statistical explanations necessary for business decisions. One of the businesses that operates for the needs of all elements is the Crude Palm Oil (CPO) commodity industry. Where the CPO price can be forecast using time series because it uses a series at the time available in fact. In this paper, 599 data of CPO price data were crawled from September 10, 2019 to April 30, 2021, then divided into 560 training data and 39 testing data. In this case, testing was carried out in measuring accuracy using MAPE in forecasting CPO prices. with time series getting 0.01781302% while accuracy is also measured by MAPE combined with detection rate gaining a percentage of 0.501031843%. This indicates that when forecasting with time series on CPO price data, the best accuracy is calculated using MAPE without any combination with other techniques.