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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

Article Details

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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References

  1. BPS Kabupaten Mojokerto, “Luas Panen dan Produksi Padi Kabupaten Mojokerto 2021,” BPS Kabupaten Mojokerto, https://mojokertokab.bps.go.id/publication/2022/03/31/456f54befab1c817cb17617d/luas-panen-dan-produksi-padi-kabupaten-mojokerto-2021.html (accessed Apr. 27, 2022).
  2. Isnaini, Sirojuzilam and A. Purwoko, “Peranan Sistem Informasi Padi Better Rice Initiative Asia (BRIA) dalam Mendukung Ketersediaan Pangan Daerah,” Jurnal Serambi Engineering, vol. 3, no. 2, pp. 306–320, Feb. 2018.
  3. I.G.N.Y. Putra, M. Antara, and I.D.P.O. Suardi, “Efisiensi Penggunaan Faktor-Faktor Produksi pada Usaha Tani Padi Subak Carik Tangis Wongaya Gede Tanaban–Bali,” Jurnal Manajemen Agribisnis, vol. 6, no. 1, pp. 70–77, May 2018, doi: 0.24843/JMA.2018.v06.i01.p10.
  4. M.W.K. Rini, W. Budiasa, and Widhianthini, “Studi Kelayakan Investasi Pabrik Penggilingan Padi Terintegrasi,” Jurnal Manajemen Agribisnis, vol. 9, no. 1, pp. 235–248, May 2021, doi: 10.24843/JMA.2021.v09.i01.p02.
  5. D.W. Triscowati, B. Sartono, A. Kurnia, D.D. Domiri, and A.W. Wijayanto, “Klasifikasi Fase Tanam Padi Menggunakan Supervised Random Forest Pada Data Multitemporal Citra Landsat-8,” in Prosiding Seminar Nasional Penginderaan Jauh 2019, 2019, pp. 1–11.
  6. H. Putra and N.U. Walmi, “Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation,” Jurnas Nasional Teknologi dan Sistem Informasi, vol. 6, no. 2, pp. 100–107, Aug. 2020, doi: 10.25077/TEKNOSI.v6i2.2020.100-107.
  7. Kadir and O.R. Prasetyo, “Peramalan Luas Panen Padi Indonesia dengan Model ETS (Error, Trend, Seasonal),” Jurnal Matematika Dan Statistika Serta Aplikasinya, vol. 9, no. 1, pp. 7–15, Jan.–Jun. 2021, doi: 10.24252/msa.v9i1.19666.
  8. F.T. Ahsani and D. Ardian, “Analisis Faktor yang Memengaruhi Swembada Beras di Indonesia Tahun 2018,” in Prosiding Seminar Nasional Official Statistics, 2019, pp. 196–201, doi: 10.34123/semnasoffstat.v2019i1.256.
  9. K. Ruslan, “Improving Indonesia’s Food Statistics through the Area Sampling Frame Method,” Center for Indonesian Policy Studies, Jakarta, Indonesia, Jul. 2019, doi: 10.35497/287781.
  10. D. Febrimeli, A.Z. Siregar, and M.B. Triyoga, “Analisa Perubahan Sosial dalam Medernisasi Budidaya Tanaman Padi di Kecamatan Secanggang Kabupaten Langkat Provinsi Sumatera Utara,” Jurnal Sosial Ekonomi Pertanian, vol. 16, no. 3, pp. 272–286, Oct. 2020, doi: 10.20956/jsep.v16i3.11941.
  11. N. Paramitasari and B. Magdalena, “Penerapan Merek dan Digital Marketing dalam Produksi Pangan Beras Desa Banjarsari,” Jurnal Abdi Masyarakat Saburai (JAMS), vol. 2, no. 1, pp. 42–51, Apr. 2021, doi: 10.24967/jams.v2i01.1226.
  12. K. Khudori, “Kaji Ulang Kebijakan Perberasan Rice Policy Review,” Jurnal Pangan, vol. 28, no. 1, pp. 57–72, Apr. 2019, doi: 10.33964/jp.v28i1.421.
  13. M.N. Fawaiq, A. Jazuli, and M. M. Hakim, “Prediksi Hasil Pertanian Padi Di Kabupaten Kudusdegan Metode Brown’s Double Exponential Smoothing,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 4, no. 2, pp. 78–87, Dec. 2019, doi: 10.29100/jipi.v4i2.1421.
  14. A.A. Suryanto and A. Muqtadir, “Penerapan Metode Mean Absolute Error dalam Algoritma Regresi Linier untuk Prediksi Produjsu Padi,” Saintekbu, vol. 11, no. 1, Feb. 2019, doi: 10.32764/saintekbu.v11i1.298.
  15. U. Ramdhani, “Efisiensi Luas Panen Padi di Provinsi Sulawesi Selatan,” Ziraa’ah Majalah Ilmiah Pertanian, vol. 44, no. 1, pp. 72–76, Feb. 2019, doi: 10.31602/zmip.v44i1.1573.
  16. K. Kadir and O. R. Prasetyo, “Peramalan Luas Panen Padi Indonesia Dengan Model ETS (Error, Trend, Seasonal),” J. MSA ( Mat. dan Stat. serta Apl. ), vol. 9, no. 1, p. 7, 2021, doi: 10.24252/msa.v9i1.19666.