The Best  Forecasting Model For Cassava Price

This study aims to analyze and select the most accurate forecasting for predicting cassava prices in Indonesia. The data used is monthly data during the period of 2009 to 2017. This predicting uses the forecasting model, such as Moving Average, Exponential Smoothing, and Decomposition. Selecting the models found by comparing the smallest values of MAPE, MAD, and MSD. Therefore, it concluded that the Moving Average model is the most appropriate to Forecasting the price of cassava. Keywords : Selection, Forecasting model, cassava, prices

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PENDUGAAN MODEL PERAMALAN HARGA EKSPOR KOPI INDONESIA

AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian ◽

10.37676/agritepa.v7i1.1002 ◽

2020 ◽

Vol 7 (1) ◽

pp. 22-30

Author(s):

FAUZI EMLAN ◽

Wawan Eka Putra ◽

Andi Ishak ◽

Herlena Bidi Astuti

Keyword(s):

Trend Analysis ◽

Moving Average ◽

Export Price ◽

Exponential Smoothing ◽

Forecasting Model ◽

Price Forecasting ◽

Monthly Data ◽

Forecasting Models ◽

Model Based ◽

Single Exponential

ABSTRACT This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values compared to other models. Keywords: Price, Coffee, Forecasting, Export

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UPAYA PENINGKATAN PRODUKSI KOPI DENGAN PANEN PETIK MERAH DI KABUPATEN REJANG LEBONG

AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian ◽

10.37676/agritepa.v7i1.1001 ◽

2020 ◽

Vol 7 (1) ◽

pp. 31-40

Author(s):

Afrizon Afrizon ◽

Andi Ishak ◽

Darkam Mussaddad

Keyword(s):

Trend Analysis ◽

Moving Average ◽

Export Price ◽

Exponential Smoothing ◽

Forecasting Model ◽

Price Forecasting ◽

Monthly Data ◽

Forecasting Models ◽

Model Based ◽

Single Exponential

This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values compared to other models. Keywords: Price; Coffee; Forecasting; Export

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Forecasting Model Selection of Curly Red Chili Price at Retail Level

Indonesian Journal of Agricultural Research ◽

10.32734/injar.v2i1.859 ◽

2019 ◽

Vol 2 (1) ◽

pp. 1-12

Author(s):

Ketut Sukiyono ◽

Miftahul Janah

Keyword(s):

Moving Average ◽

Price Policy ◽

Exponential Smoothing ◽

Future Price ◽

Forecasting Model ◽

Price Forecasting ◽

Monthly Data ◽

Forecasting Models ◽

Double Exponential ◽

Double Exponential Smoothing

Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).

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