Forecasting National Rice Production Using Autoregressive Distributed LAG and K-Means Clustering
Keywords: ARDL, clustering, forecasting, rice production, time series
Abstract
Indonesia as an agricultural country faces various obstacles in its development. The increasing population, decreasing agricultural land area, and differences in potential in each province are the biggest problems in rice production. The limitations of research in general in presenting differences in rice production characteristics between provinces are the urgency discussed in this study. The purpose of this study is to develop a rice production forecasting model in Indonesia using the ARDL approach combined with the K-Means clustering technique. The variables used in this study are rice production, harvested area, and farmers’ terms of trade (NTP). Forecasting is carried out for the period 2024 with the aim of obtaining an accurate estimate of rice production in Indonesia. The results show that the ARDL model integrated with K-Means clustering provides highly accurate rice production forecasts, as indicated by low MAPE and RMSE values. In particular, Cluster 2 achieves the best performance with a MAPE of 0.36% and an RMSE of 3,860.40, followed by Cluster 1 (MAPE 2.17%; RMSE 33,192.31) and Cluster 3 (MAPE 5.88%; RMSE 40,577.10). In contrast, the national ARDL model without clustering records much larger errors (MAPE 14.71%; RMSE 582,062.00), confirming that clustering substantially improves forecasting accuracy and produces prediction patterns that closely match actual rice production.
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