Optimasi Fungsi Pembelajaran Jaringan Saraf Tiruan dalam Meningkatkan Akurasi pada Prediksi Ekspor Kopi Menurut Negara Tujuan Utama

Abstract

In the learning process carried out by Backpropagation the learning function is important in finding optimal results. This study aims to optimize the learning function of artificial neural networks in increasing the accuracy of coffee export predictions according to the main destination countries as research objects. This study applies the learning function to weights in Matlab, namely Gradient Descent with Adaptive Learning Rate (traingda), Gradient Descent with Momentum (traingdm), and Gradient Descent with Momentum and Adaptive Learning Rate (trainingdx) using several hidden layers, namely 15,30 and 45. Based on a series of trials conducted, the results of the study show that by implementing the Gradient Descent learning function with an Adaptive Learning Rate (trainingda) with a hidden layer of 30 it is capable of training neural networks with a better level of optimization, performing 143 iterations which produces a truth accuracy of 83%. When compared with the use of other learning functions that only last with an accuracy of no more than 78%. In general, it can be concluded that the optimization of the Gradient Descent learning function with Adaptive Learning Rate (trainda) can be applied to predict coffee exports according to the main destination countries, because the iterative process carried out to achieve convergence in increasing accuracy performs well

Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation

“…Penelitian ini memfokuskan perhatian pada peran penting metode Backpropagation dalam meningkatkan akurasi prediksi (Windarto et al, 2021). Penyesuaian bobot dan bias dalam konteks deep learning menjadikan model Backpropagation sebagai elemen kunci dalam strategi dimana hal ini dapat memberi pemahaman lebih mendalam terkait pergerakan nilai tukar dan aspek-aspek terkait (Ridho et al, 2023).…”

Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing Di Indonesia

“…Pada Neural Network pada saat melakukan sebuah proses menggunakan bobot sinaptik untuk menentukan tindakan saraf, kemudian tindakan saraf menentukan keluaran jaringan dan keluaran jaringan menentukan kegalan jaringan [21]. Teknik jaringan saraf tiruan dengan mengatur bobot dan bias pada setiap lapisan jaringan berdasarkan selisih antara hasil prediksi dan nilai tujuan [22]. Salah satu jenis metode yang melakukan pelatihan jaringan saraf tiruan dengan pemeriksaan adalah back-propagation [23].…”