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Abstract

The agricultural sector is crucial for achieving SDG 2, addressing hunger, ensuring food security, and promoting sustainable agriculture. This study applies the Area Sample Framework (ASF) to estimate rice harvest yields in Mojokerto Regency, emphasizing the importance of accurate agricultural data for effective policy formulation and SDG support. ASF utilizes square segment-based sampling units to provide potential rice harvest area data. However, research on the accuracy of ASF-derived data, especially for predicting the next year’s rice harvest, is limited. This study evaluates ASF data accuracy for 2019, 2020, and 2021 using three key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results show varying accuracy each year. In 2019, MAPE was 91%, with MAE and RMSE around 2,714.75 ha and 15,463,954.79 ha, indicating high accuracy. Conversely, in 2021, MAPE rose to 107%, with MAE and RMSE near 2,680.09 ha and 14,677,241.22 ha, revealing lower prediction accuracy. This study underscores the importance of continuous monitoring and enhancing data accuracy to support sustainable agriculture and food security, especially in regions like Mojokerto Regency. Further research should investigate factors affecting harvested area efficiency and ways to improve prediction accuracy for effective SDG implementation.  

Keywords

Agriculture Harvested area Production data ASF MAPE MAE RMSE

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Author Biographies

Fera Yuliana, Universitas Islam Majapahit

Informatics Enginering

Arief Krisnah Sholikhan, Universitas Islam Majapahit

Informatics Enginering

Yesy Diah Rosita, IT Telkom Purwokerto

Artificial Intelligence, Digital Support System, Geographic Information System, Digital Image Processing, Internet of Things

How to Cite
Nugroho, M. A., Yuliana, F. ., Sholikhan, A. K., & Rosita, Y. D. (2023). Analyzing Potential Rice Harvest Area in Mojokerto Regency in 2021 Using Area Sample Framework (ASF). Enthusiastic : International Journal of Applied Statistics and Data Science, 3(2), 151–162. https://doi.org/10.20885/enthusiastic.vol3.iss2.art3
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