Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga
DOI:
https://doi.org/10.35143/jkt.v9i1.5917Keywords:
Panen Padi, Prediksi, Random ForestAbstract
Minanga Village, Bambang District, Mamasa Regency, the people of which cultivate rice plants, usually the yields fluctuate each season, which often decreases or increases unstable. This research is expected to assist in predicting rice yields in accordance with pre-existing criteria and data such as land area, number of seeds, type of fertilizer, rainfall, pests and weeds, pest and weed control, and the rice planting system used (jajar legowo) , by applying the Random Forest Regression algorithm. Evaluation of algorithm performance is measured using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R²), the results of the Random Forest model obtained from 9 trees, the variable that has the highest value on the variable importance is the variable land area. So that from this model an accuracy value of 95.11% is obtained, the MAPE value in this model is 4.884%, the RMSE value is 0.250 and the R² value is 0.99.Downloads
References
A. Basit, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Hasil Panen Padi,” J. Tek. Inform. Kaputama, vol. 4, no. 2, pp. 208–213, 2020.
S. S. Defiyanti Muhammad; Dermawan, Budi Arif, “Algoritma C4.5 Untuk Memprediksi Produktivitas Tanaman Padi Di Karawang,” Inform. Pertan., no. Vol 28, No 2 (2019): DESEMBER 2019, pp. 103–110, 2019, [Online]. Available: http://ejurnal.litbang.pertanian.go.id/index.php/IP/article/downloadSuppFile/9923/827
H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 364, 2019, doi: 10.22146/jnteti.v8i4.537.
I. L. Mulyahati, “Implementasi Machine Learning Prediksi Harga Sewa Apartemen Menggunakan Algoritma Random Forest Melalui Framework Website Flask Python,” Universitas Islam Indonesia, 2020. [Online]. Available: papers3://publication/uuid/143B9B2C-186A-4872-8DC1-638AAC4D36C3
M. Mirqoatul Mafa’atiih, “Implementasi Artificial Inteligence Untuk Memprediksi Harga AIRBNB Menggunakan Metode Random Forest Dan Penerapan Web Application Menggunakan Flask.,” Universitas Islam Indonesia, 2020. doi: 10.53730/ijhs.v6ns6.9986.
i. Y. Supartha, g. Wijaya, and g. M. Adnyana, “Aplikasi Jenis Pupuk Organik pada Tanaman Padi Sistem Pertanian Organik,” E-Jurnal Agroekoteknologi Trop., vol. 1, no. 2, pp. 98–106, 2012.
F. Azizah, S. Suwarsito, and E. Sarjanti, “Pengaruh Pola Curah Hujan Terhadap Produktivitas Padi di Kecamatan Bukateja Kabupaten Purbalingga,” Sainteks, vol. 18, no. 1, p. 1, 2021, doi: 10.30595/sainteks.v18i1.10567.
C. V Donggulo, I. M. Lapanjang, and U. Made, “Growth and Yield of Rice (Oryza sativa L.) under Different Jajar Legowo System and Planting Space,” J. Agrol., vol. 24, no. 1, pp. 27–35, 2017, [Online]. Available: http://jurnal.untad.ac.id/jurnal/index.php/AGROLAND/article/view/8569
S. Saadah and H. Salsabila, “Prediksi Harga Bitcoin Menggunakan Metode Random Forest: (Studi Kasus: Data Acak Pada Masa Pandemic Covid-19),” J. Komput. Ter., vol. 7, no. 1 SE-, pp. 24–32, Jun. 2021, doi: 10.35143/jkt.v7i1.4618.
T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, “How many trees in a random forest?,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7376 LNAI, no. July 2012, pp. 154–168, 2012, doi: 10.1007/978-3-642-31537-4_13.
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