Penerapan Algoritma Jaringan Saraf Tiruan Metode Backpropagation untuk Memprediksi Jumlah Nilai Ekspor di Provinsi NTB

Putra, Biondi Bagasta Wiko; Albar, Moh. Ali; Irmawati, Budi

doi:10.29303/jtika.v1i2.50

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…The 5 hidden layer experiments produce good training results. This is comparable to research by [15], which attempted the same number of hidden. This demonstrates that having too few hidden layer neurons is bad because it can slow down the learning process, so the more hidden layers there are, the better the learning process and the lower the MSE value.…”

Section: Discussionsupporting

confidence: 69%

“…Formation of artificial neural networks through network training with artificial neural network architectures designed by testing hidden layers 3, 4, and 5 [15]. This training process is carried out in an experiment to see the effect of the learning rate value with a range (0.1 -0.9) for each hidden layer [15]. It will conduct iteration trials to find the optimal number of iterations.…”

Section: Backpropagation Modeling Determinationmentioning

confidence: 99%

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Application of the Backpropagation Method to Predict Rainfall in North Sumatra Province

Nabila

Arnita

Fitria

et al. 2023

BAREKENG: J. Math. & App.

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Natural disasters are to blame for the high level of community loss. This is due to the community's lack of information about potential disasters around them. As a result, public understanding of disaster response is extremely low. As a result, weather information is critical for the smooth operation of human activities and activities, such as determining the amount of rainfall. The goal of this research is to identify the best model for predicting rainfall in North Sumatra Province and to forecast rainfall trends for the coming year. The rainfall time series data used in this study were collected from six stations in North Sumatra Province over the last ten years, including the Sibolga Meteorological Station, Aek Godang Meteorological Station, and Silangit Meteorological Station. Backpropagation is used in this study. Backpropagation is one of the methods used in artificial neural networks, which are usually divided into three layers: an input layer, a hidden layer, and an output layer connected by weights. During the training stage, the learning rate, iteration, and number of nodes in the hidden layer were all tested. Following the training process, the best model will be used for testing. The best model was obtained using rainfall data from North Sumatra Province, with an optimal iteration of 1000 iterations, an optimal learning rate of 0.1 in the learning rate trial, and the best number of hidden 5 nodes. During the testing, the MSE values were 0.047 and 0.022, respectively, and the MSE squared value was 0.0022 and 0.00049.

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Section: Discussionsupporting

confidence: 69%

Section: Backpropagation Modeling Determinationmentioning

confidence: 99%

Application of the Backpropagation Method to Predict Rainfall in North Sumatra Province

Nabila

Arnita

Fitria

et al. 2023

BAREKENG: J. Math. & App.

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“…Sementara itu, prediksi (forecasting) adalah suatu seni dan ilmu yang digunakan untuk mengestimasikan atau memperkirakan kejadian-kejadian yang akan terjadi di masa depan (Amrustian, Afrizal, Widayat, & Wirawan, 2022). Untuk melakukan prediksi ini, biasanya melibatkan pengumpulan data historis yang relevan dan menganalisisnya untuk memproyeksikan ke masa yang akan datang (Wiko Putra, Albar, and Irmawati 2019). Metode yang digunakan dapat berupa pembentukan model matematis yang mempertimbangkan aspek-aspek penting, atau juga menggunakan pendekatan prediksi yang bersifat subyektif berdasarkan intuisi yang baik dan pengalaman peneliti (Alfandi & Hatuaon Sihite, 2022).…”

Section: Pendahuluanunclassified

Prediksi Nilai Ekspor Pulp di Indonesia Mengunakan Metode Long Short Term Memory

Silaen,

Simanjuntak,

Tarigan

et al. 2023

JTIK

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Ekspor pulp merupakan kegiatan ekonomi penting bagi perusahaan dan pemerintah, yang membutuhkan informasi akurat mengenai permintaan pasar dan strategi bisnis yang tepat. Metode Long Short Term Memory (LSTM) digunakan untuk memprediksi hasil produksi pulp di masa depan dengan memanfaatkan data historis dan faktor-faktor yang mempengaruhinya. Tahapan penelitian meliputi studi literatur untuk memahami metode LSTM, pengumpulan data, seleksi dan transformasi data untuk mempersiapkan dataset yang akan digunakan, serta visualisasi data ekspor untuk mendapatkan wawasan yang lebih baik. Selanjutnya, metode LSTM diterapkan dengan langkah-langkah pembentukan model, pelatihan model, prediksi nilai ekspor, dan evaluasi hasil prediksi. Hasil penelitian ini untuk nilai Root Mean Squared Error (RMSE) terhadap produk Jumbo Roll Tissue, Napkin Tissue, Multi Purpose Tissue, dan Facial Tissue berturut-turut adalah 2.52, 1.88, 2.77, dan 2.67. Semakin kecil nilai RMSE, semakin baik performa model. Nilai RMSE yang kecil pada setiap produk menunjukkan bahwa model memiliki performa yang baik dalam memprediksi semua produk. Dengan prediksi yang lebih akurat, perencanaan produksi dan persediaan bahan baku dapat dilakukan dengan lebih efisien dan efektif, sehingga mengoptimalkan produktivitas dan mengurangi biaya produksi.Hasil penelitian ini diharapkan dapat membantu masyarakat dan pemerintah dalam pengambilan keputusan strategis terkait produksi pulp dan kebijakan ekspor. Selain itu, penelitian ini juga diharapkan dapat meningkatkan efisiensi pemasaran dan distribusi produk, meningkatkan pengetahuan tentang pasar dan produk, serta membuka peluang pasar baru untuk produk pulp.

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“…Export is an activity of sending goods or other products from one country to another (Wiko Putra et al, 2019). North Sumatra is one of the provinces in Indonesia, where North Sumatra Province also participates in international trade, especially in exporting (S. M. Damanik et al, 2020).…”

Section: Export and Importmentioning

confidence: 99%

Implemetasi algoritma fletcher-reeves dalam menganalisa nilai ekspor menurut golongan sitc

Wulandari

Wahyudi²,

Pujiastuti³

et al. 2022

Computational

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Indonesia has a lot of natural resources, so Indonesia uses them by conducting trade activities between countries. Export is a movement to sell or send goods from within the country to abroad. Conjugateigradient Fletcher-Reeves Algorithm According to some references, it is an appropriate development technique compared to the backpropagation strategy because this strategy can speed up the preparation time to arrive at the minimum convergence value. Furthermore, this research shows whether the algorithm performs well and can provide productive assembly results when used to solve problems because the value corresponds to the Standard International Trade Classification (SITC) class. The prediction models used are 5-10-1, 5-15-1, 5-20-1, 5-25-1 and 5-30-1. MSE of 0.00287273, the minimum among the other four models. The model can be used because it produces a fast combination and a fairly short period

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Penerapan Algoritma Jaringan Saraf Tiruan Metode Backpropagation untuk Memprediksi Jumlah Nilai Ekspor di Provinsi NTB

Cited by 3 publications

References 2 publications

Application of the Backpropagation Method to Predict Rainfall in North Sumatra Province

Application of the Backpropagation Method to Predict Rainfall in North Sumatra Province

Prediksi Nilai Ekspor Pulp di Indonesia Mengunakan Metode Long Short Term Memory

Implemetasi algoritma fletcher-reeves dalam menganalisa nilai ekspor menurut golongan sitc

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