Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera
DOI:
https://doi.org/10.47065/jieee.v1i4.382Keywords:
Performance; Eggs; Machine Learning; ANN; DataAbstract
Laying hens eggs are one of the livestock commodities that make a very large contribution to the supply of eggs as a community need. Therefore, it is necessary to predict the egg production of laying hens in the future so that in the future the need for eggs in Indonesia is stable and can meet the demands of the Indonesian people. The method used in this research is a machine learning algorithm, namely Polak-Ribiere which is one of the artificial neural network methods that is often used to predict data. This study does not discuss the prediction results, but will discuss the ability of the Machine Learning algorithm to make predictions based on the egg production dataset of laying hens obtained from the Central Statistics Agency. The research data used is data on the production of laying hens in Sumatra from 2015-2020. Based on this data, network architecture models will be determined, including 4-5-1, 4-10-1, 4-15-1, 4-20-1, and 4-25-1. Of the five models, training and testing were carried out first and then obtained the results that the best architectural model was 4-25-1 with 0.03144841, the lowest among the other 4 models. So it can be concluded that the model can be used to predict the egg production of laying hens.
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References
A. A. Budiarto, B. Fatkhurrozi, and I. Setyowati, “Implementasi Operator Canny Identifikasi Fertilitas Telur Ayam Buras,” Theta Omega J. Electr. Eng. Comput. Inf. Technol., vol. 1, no. 2, pp. 1–7, 2020.
W. Artini and E. Rusmanto, “Ragam Konsumsi Pangan Masyarakat Pedesaan Di Desa Margopatut Kecamatan Sawahan Kabupaten Nganjuk,” Agrinika, vol. 1, no. 1, pp. 27–43, 2017.
M. Kristina and S. Sulantiwi, “Sistem pendukung keputusan menentukan kualitas bibit ikan gurame di pekon Sukosari menggunakan Aplikasi Visual Basic 6.0,” J. Technol. Accept. Model., vol. 4, pp. 26–33, 2015.
S. A. Wulandari and R. R. E. Fitri, “Hubungan Antara Persepsi Dengan Preferensi Konsumen Terhadap Tempe Di Pasar Angso Duo Kota Jambi,” J. MeA (Media Agribisnis), vol. 5, no. 1, p. 47, 2020, doi: 10.33087/mea.v5i1.64.
R. Frisca Siahaan, “Mengawal Kesehatan Keluarga Melalui Pemilihan Dan Pengolahan Pangan Yang Tepat,” J. Kel. Sehat Sejah., vol. 15, no. 2, pp. 57–64, 2017, doi: 10.24114/jkss.v15i2.8775.
M. S. Wibawa, “Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan,” J. Sist. dan Inform., vol. 11, no. 2, pp. 1–8, 2016.
A. Mustofa and N. Suhartatik, “Meningkatkan Imunitas Tubuh Dalam Menghadapi Pandemi Covid-19 Di Karangtaruna Kedunggupit, Sidoharjo, Wonogiri, Jawa Tengah,” SELAPARANG J. Pengabdi. Masy. Berkemajuan, vol. 4, no. 1, p. 317, 2020, doi: 10.31764/jpmb.v4i1.3100.
N. Z. Purba, A. Wanto, and I. O. Kirana, “Implementation of ANN for Prediction of Unemployment Rate Based on Urban Village in 3 Sub-Districts of Pematangsiantar,” Int. J. Inf. Syst. Technol., vol. 3, no. 1, pp. 107–116, 2019.
A. Wanto et al., “Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin,” J. Phys. Conf. Ser., vol. 1255, no. 1, 2019, doi: 10.1088/1742-6596/1255/1/012013.
M. Julham, S. Sumarno, F. Anggraini, A. Wanto, and S. Solikhun, “Penerapan Jaringan Syaraf Tiruan dalam Memprediksi Tingkat Kriminal di Kabupaten Simalungun Menggunakan Algoritma Backpropagation,” BRAHMANA J. Penerapan Kecerdasan Buatan, vol. 1, no. 1, pp. 64–73, 2019, doi: 10.30645/brahmana.v1i1.9.
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