Andreas Siagian, Nelva Meyriani Br Ginting Br Ginting, Riska Elsa Rianty Sinaga, Muhammad Nazarul Yanis, Reflianta Br. Sinaga, Marino Manik, Surtan Hasibuan

The ANALYSIS OF FORECASTING OF FARMER EXCHANGE RATE FOR THE FOOD CROP SUBSECTOR IN NORTH SUMATRA PROVINCE USING ARIMA METHOD

Introduction

The analysis of forecasting of farmer exchange rate for the food crop subsector in north sumatra province using arima method. Forecast Farmer Exchange Rate (NTP) for North Sumatra's food crop subsector (2025-2027) using ARIMA. Analysis shows increasing trend, but farmers remain below welfare threshold.

Abstract

One of the measurements the level of farmer welfare is by calculating the Farmer Exchange Rate (NTP). This study aims to analysis of forecasting and describe the results of forecasting the farmer exchange rate (NTP) for the food crop subsector in 2025-2027 using the ARIMA method. This research employs descriptive and econometric analysis using time series NTP for the food crop subsector from January 2020 to December 2024 in North Sumatra Province, obtained from the BPS SUMUT website. This study shows that the best forecasting model is ARIMA (2,1,0) and the forecasting results show an increasing trend, but the NTP value < 100 percent, so food crop farmers in North Sumatra are not yet wealthy.

Review

This paper addresses a highly relevant topic concerning farmer welfare, specifically focusing on the Farmer Exchange Rate (NTP) for the food crop subsector in North Sumatra Province. The objective to forecast NTP using the ARIMA method for 2025-2027 is clear and directly tackles an important aspect of agricultural policy and economic stability. The study's focus on a specific region and subsector allows for targeted insights, and the use of readily available government data from BPS SUMUT lends credibility to the data source. Methodologically, the study employs time series analysis with the ARIMA model, which is a standard approach for forecasting such economic indicators. The identification of ARIMA(2,1,0) as the best model is a specific and valuable outcome of the analysis. However, the abstract indicates that while an increasing trend is projected, the NTP value remains below 100 percent, leading to the conclusion that food crop farmers in the region are "not yet wealthy." While this conclusion is direct, the abstract could benefit from a brief explanation of the implications of an NTP value below 100 (i.e., prices received by farmers are lower than prices paid for household and and production needs). Furthermore, using a five-year data set (January 2020 to December 2024) to forecast three years into the future (2025-2027) might raise questions regarding the robustness and long-term stability of the model, especially considering potential market fluctuations and external shocks over such periods. The findings of this research offer critical insights for policymakers and stakeholders involved in agricultural development in North Sumatra. The projected increasing trend, even if NTP remains below 100, suggests a potential for improvement, albeit one that still necessitates intervention to enhance farmer welfare. Future work could potentially explore the underlying factors contributing to the NTP staying below 100, such as input costs, market prices for food crops, or government subsidies, which might help inform more effective policy recommendations. Comparing the ARIMA model's performance with other forecasting techniques or incorporating exogenous variables could further strengthen the predictive power and policy relevance of subsequent studies. The paper lays a solid foundation for further investigations into the economic well-being of food crop farmers in the region.

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