Tugas Akhir Informatika

PENERAPAN ALGORITMA JARINGAN SARAF TIRUAN METODE BACKPROPAGATION UNTUK MEMPREDIKSI JUMLAH NILAI EKSPOR DI PROVINSI NTB

---

This paper presents the application of the Backpropagation method of the Artificial Neural Network algorithm in the case study to predict the amount of export value in NTB province. This forecasting process uses two scenarios namely forecasting the total value of total exports in NTB province and forecasting the amount of export value based on commodity. The activation functions used are Sigmoid Binary and ReLU - Linear. Based on the results of the system tests that have been carried out, the Sigmoid Binary activation function shows that the best network architecture is 12-4-1, the best learning rate is 0,3 and the best error limit is 0,0015, which in the test phase produces an MSE value of 0,0161 and the MAPE value of 30,53%, while the ReLU-Linear activation function shows that the best network architecture is 12-5-1, the best learning rate is 0,1 and the best error limit is 0,0012, which is at the test phase produce MSE value of 0,0309 and the MAPE value of 53,04%. Because the MAPE value generated in the system testing using each activation function does not have a value of less than 20%, the use of Backpropagation in the study is not suitable.

---

Ketersediaan

2018159

611.018.Bio.p

Tersedia

Informasi Detail

Judul Seri

-

No. Panggil

611.018.Bio.p

Penerbit

Universitas Mataram : Fakultas Teknik Unram., 2019

Deskripsi Fisik

-

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

611.018

Tipe Isi

other

Tipe Media

other

Tipe Pembawa

-

Edisi

Edisi 1 Jilid 1

Subjek

Intelegensi buatan
Artificial Neural Network, Backpropagation
Value of Export, Forecasting, System Testing

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Biondi Bagasta Wiko Putra

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus login sebelum memberikan komentar