MODEL ALGORITMA RESILIENT BACKPROPAGATION DALAM MEMPREDIKSI EKSPOR BIJIH COKLAT MENURUT NEGARA TUJUAN UTAMA DALAM MENDORONG LAJU PERTUMBUHAN EKONOMI

Authors

  • Sundari Retno Andani Neno AMIK Tunas Bangsa
  • Rafiqa Dewi Rafiqa AMIK Tunas Bangsa
  • Solikhun Lihun AMIK Tunas Bangsa

DOI:

https://doi.org/10.31598/jurnalresistor.v2i2.383

Keywords:

Jaringan Saraf Tiruan, Backpropagation, Cocoa Beans

Abstract

The purpose of this study predicts the export of brown ore according to the country the main objective in driving the pace of economic growth. Cocoa beans including plantation products are exported and are very profitable for Indonesia. However, the quality of cocoa beans exported by Indonesia is known to be low. The low quality of Indonesian cocoa is due to several reasons, including rare Indonesian cocoa beans which are fermented first. Indonesia is an exporter of cocoa beans. The government must be able to predict brown ore exports in the future so that the government can take steps or policies on how to make reliable strategies in an effort to increase the export of brown ore in the future. Backpropagation is one of the ANN models that has the ability to get a balance between the ability of the network to recognize patterns used during training and the ability of the network to respond correctly to input patterns that are similar (but not the same) to the patterns used during training. After a training experiment and testing of architectural models 12-4-1, 12-8-1, 8-12-1, and 8-16-1, the best architectural model was 12-12-1 with 100% accuracy.

Downloads

Download data is not yet available.

References

A. Revi, “Jaringan Syaraf Tiruan Dalam Memprediksi Tingkat Pertumbuhan Industri Mikro Dan Kecil Berdasarkan Provinsi,†Teknika, vol. 7, no. 2, 2018.

A. Revi et al., “Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Produksi Daging Sapi Berdasarkan Provinsi,†vol. 2, pp. 297–304, 2018.

A. P. Windarto, “Implementasi Jst Dalam Menentukan Kelayakan Nasabah Pinjaman Kur Pada Bank Mandiri Mikro Serbelawan Dengan Metode Backpropogation,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, pp. 12–23, 2017.

N. Nurmila, A. Sugiharto, and E. A. Sarwoko, “Algoritma Back Propagation Neural Network untuk Pengenalan Karakter Huruf Jawa,†J. Masy. Inform. ISSN 2086-4930, vol. 1, no. 1, pp. 1–10, 2005.

M. Agustin and T. Prahasto, “Penggunaan Jaringan Syaraf Tiruan Backpropagation untuk Seleksi Penerimaan Mahasiswa Baru pada Jurusan Teknik Komputer di Politeknik Sriwijaya,†J. Sist. Inf. Bisnis, vol. 02, pp. 4–32, 2012.

M. Febrina, F. Arina, and R. Ekawati, “Peramalan Jumlah Permintaan Produksi Menggunakan Metode Jaringan Syaraf Tiruan (Jst) Backpropagation,†J. Tek. Ind., vol. 1, no. 2, pp. 174–179, 2013.

S. Kusmaryanto, “Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Wajah Metode Ekstraksi Fitur Berbasis Histogram,†J. EECCIS Vol. 8, No. 2, Desember 2014, vol. 8, no. 2, pp. 193–198, 2014.

Apriliyah and A. W. W. M, Wayan Firdaus, “Perkiraan Penjualan Beban Listrik Menggunakan Jaringan Syaraf Tiruan Resilent Backpropogation (RPROP),†J. Kursor, vol. 4, no. 2, pp. 41–47, 2008.

W. Saputra, T. Tulus, M. Zarlis, R. W. Sembiring, and D. Hartama, “Analysis Resilient Algorithm on Artificial Neural Network Backpropagation,†J. Phys. Conf. Ser., vol. 930, no. 1, 2017.

Apriliyah and M, Wayan Firdaus, A. W. W. (2008) ‘Perkiraan Penjualan Beban Listrik Menggunakan Jaringan Syaraf Tiruan Resilent Backpropogation (RPROP)’, Jurnal Kursor, 4(2), pp. 41–47. doi: 10.1089/fpd.2015.2079.

Saputra, W. et al. (2017) ‘Analysis Resilient Algorithm on Artificial Neural Network Backpropagation’, Journal of Physics: Conference Series, 930(1). doi: 10.1088/1742-6596/930/1/012035.

Downloads

Published

2019-10-28

How to Cite

Neno, S. R. A., Rafiqa, R. D., & Lihun, S. (2019). MODEL ALGORITMA RESILIENT BACKPROPAGATION DALAM MEMPREDIKSI EKSPOR BIJIH COKLAT MENURUT NEGARA TUJUAN UTAMA DALAM MENDORONG LAJU PERTUMBUHAN EKONOMI. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(2), 67-75. https://doi.org/10.31598/jurnalresistor.v2i2.383