Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Jumlah Ekspor Buah-Buahan Menurut Negara Tujuan
- Nurhidayana Nurhidayana STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
-
Irfan Sudahri Damanik
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Rafiqa Dewi AMIK Tunas Bangsa, Pematangsiantar, Indonesia (*) Corresponding Author
Abstract
This research aims to predict the number of exports of fruits by country of destination in the coming year based on data in the previous year. The application of a prediction is very important in conducting research. The method used in conducting this prediction is the artificial neural network (JST) with its Backpropagation algorithm. The architecture used in this study is 4-2-1, 4-4-1, 4-6-1, 4-8-1, 4-4-2-1, and 4-3-6-1. Test results obtained prediction of export of fruits according to destination country with 91% accuracy rate with 4-4-1 architecture
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References
A. P. Windarto, “Penerapan Datamining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering Method,” Techno.Com, vol. 16, no. 4, pp. 348–357, 2017, doi: 10.33633/tc.v16i4.1447.
A. S. Ritonga and S. Atmojo, “Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra),” J. Ilm. Teknol. Inf. Asia, vol. 12, no. 1, p. 15, 2018, doi: 10.32815/jitika.v12i1.213.
S. Solikhun, M. Safii, and A. Trisno, “Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, p. 24, 2017, doi: 10.30645/j-sakti.v1i1.26.
I. S. Purba and A. Wanto, “Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation,” Techno.Com, vol. 17, no. 3, pp. 302–311, 2018, doi: 10.33633/tc.v17i3.1769.
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Copyright (c) 2021 Nurhidayana Nurhidayana, Irfan Sudahri Damanik, Rafiqa Dewi
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