Implementation of the Random Forest algorithm to predict rice needs in DKI Jakarta

Hadi Santoso; Lukman Hakim; Afiyati Afiyati; Bambang Jokonowo

doi:10.31315/telematika.v22i1.12850

Authors

Hadi Santoso Universitas Mercu Buana http://orcid.org/0000-0002-1288-4267
Lukman Hakim Universitas Mercu Buana
Afiyati Afiyati Universitas Mercu Buana
Bambang Jokonowo Universitas Mercu Buana

DOI:

https://doi.org/10.31315/telematika.v22i1.12850

Keywords:

predict, rice, random forest

Abstract

Purpose : to build collaborative partners between government institutions and universities in food processing, especially rice, by predicting rice needs in the DKI Jakarta area.

Design/methodology/approach:

The approach in this research uses the Random Forest algorithm which functions to predict rice needs in the DKI Jakarta area.

Results: rice demand prediction application with evaluation values Mean Squared Error 207.86, Mean Absolute Error 9.43, MAPE 0.048, Root Mean Squared Error 14.4, accuracy 0.63

Originality/value/state of the art:

research using data from BAPANAS, Cipinang Main Market, with 2 datasets of rice stock, population, year and rice consumption using a random forest algorithm to predict rice needs in the DKI Jakarta area

References

H. Santoso, Hadi;Hakim, Lukman;Afiyati; Magdalena, “Jurnal abdidas,” Sos. Dampak Kenaikan Beras dengan Prediksi Kebutuhan Beras Masy. di Pasar Induk Cipinang dengan Kerjasama Badan Pangan Nas., vol. 5, no. 2, pp. 90–96, 2024.

M. F. F. Mardianto, E. Tjahjono, M. Rifada, A. Herawanto, A. L. Putra, and K. A. Utama, “The Prediction of Rice Production in Indonesia Provinces for Developing Sustainable Agriculture,” J. First Int. Conf. Food Agric., vol., no., pp. 325–333, 2018.

Hakim, Lukman;Santoso, Hadi; yusuf, M “Jurnal abdidas,” Sos. Peran Teknol. Artif. Intel. untuk Klasifikasi Status Sos. Masy. DKI Jakarta, vol. 5, no. 3, pp. 97–102, 2024.

M. P. J. J, “Pasokan Berkurang, Pasar Beras Tertekan,” Kompas. Accessed: Nov. 23, 2023. [Online]. Available: https://www.kompas.id/baca/ekonomi/2023/08/13/pasokan-berkurang-pasar-beras-tertekan

Y. Yulida and M. A. Karim, “Prediction of rice consumption in South Kalimantan by considering population growth rate,” IOP Conf. Ser. Earth Environ. Sci., vol. 758, no. 1, 2021, doi: 10.1088/1755-1315/758/1/012022.

Badan Pangan Nasional, “Dataset Permintaan beras,” 2023.

and W. Wachdijono, Dwi Purnomo, “Rice Demand and Its Predictions as a Basis for Realizing Food Security Programs in Higher Education Institutions,” 1st Int. Agric. Nutraceutical, andFoodScience 2022 “PraisingThe Trop. Resour. Glorif. BiodiversityPotentialofNusantara” November9-10th, no. 5, pp. 103–117, 2022, [Online]. Available: https://jurnal.untidar.ac.id/index.php/icanfs/article/view/6640

T. Online, N. Nur, and F. Wajidi, “Jurnal Politeknik Caltex Riau Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga,” J. Komput. Terap., vol. 9, no. 1, pp. 58–64, 2023.

K. Choudhary, W. Shi, Y. Dong, and R. Paringer, “Random Forest for rice yield mapping and prediction using Sentinel-2 data with Google Earth Engine,” Adv. Sp. Res., vol. 70, no. 8, pp. 2443–2457, 2022, doi: 10.1016/j.asr.2022.06.073.

P. Padi, “Forecasting padi dan konsumsi Beras Jawa Timur,” vol. 20, 2023.

M. Adjie Setyadj, A. Faqih, and Y. Arie Wijaya, “Peramalan Harga Komoditas Beras Di Kalimantan Timur Menggunakan Algoritma Neural Network,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 320–324, 2023, doi: 10.36040/jati.v7i1.6327.

BPN, “Laporan Beras DKI Jakarta,” Jakarta, 2024.

E. Pramudianzah, Y. S. Triana, and R. Budiarto, “Analysis of Waste Transportation Volume in Jakarta Province using Linear Regression and Random Forest Regression,” ACM Int. Conf. Proceeding Ser., vol. i, 2022, doi: 10.1145/3557738.3557876.

T. A. U. Azmi, L. Hakim, D. C. R. Novitasari, and W. D. U. D. Utami, “Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review,” Telematika, vol. 20, no. 1, p. 117, 2023, doi: 10.31315/telematika.v20i1.8868.

D. S. B M, D. C. N K, D. P. T, and D. R. R, “Rice and Wheat Yield Prediction in India Using Decision Tree and Random Forest,” Comput. Intell. Mach. Learn., vol. 3, no. 2, pp. 1–8, 2022, doi: 10.36647/ciml/03.02.a001.