Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network

Putri Aulia Rahmah; Memi Nor Hayati; Ariyanti Cahyaningsih

doi:10.29303/ijasds.v2i1.5855

Authors

Putri Aulia Rahmah Program Studi Statistika, Universitas Mulawarman, Indonesia
Memi Nor Hayati Program Studi Statistika, Universitas Mulawarman, Indonesia
Ariyanti Cahyaningsih Badan Pusat Statistik Provinsi Kalimantan Timur, Indonesia

DOI:

https://doi.org/10.29303/ijasds.v2i1.5855

Keywords:

Farmer Exchange Rate, Forecast, Neural Network

Abstract

The farmer exchange rate (NTP) is a significant indicator for measuring the purchasing power of Indonesian farmers, who are the main actors in the agricultural sector. This is because the agricultural sector is one of the main sectors in Indonesia, one of which is in East Kalimantan Province. This study aims to predict and forecast the NTP of East Kalimantan Province using the Neural Network (NN) method with the backpropagation algorithm. The data used is the NTP data of East Kalimantan Province for the period January 2020 to September 2024 obtained from the BPS of East Kalimantan Province. This study tested 5 NN architecture models with different numbers of layers in the hidden layer, namely 1, 2, 3, 4, and 5 layers in the hidden layer. The study was conducted using 1 input variable, a learning rate of 0.01, a maximum of 10,000 iterations, and a threshold of 0.5. Based on the training process that has been carried out, it was concluded that the best NN architecture that can be used to forecast the NTP of East Kalimantan Province is NN with 5 layers in the hidden layer with a MAPE of 2.087%.

References

Andika, F., Sari, R. P., & Nurviana. (2024). Perbandingan Model Chen dan Lee pada Metode Fuzzy Time Series untuk Peramalan Nilai Tukar Petani (NTP) di Provinsi Aceh. JSMS: Jurnal Sains Matematika dan Statistika, 10(1), 71–84.

Aswi, & Sukarna. (2006). Analisis Runtun Waktu Aplikasi dan Teori. Andira Publisher.

Ayun, Q., Kurniawan, S., & Saputro, W. A. (2020). Perkembangan Konversi Lahan Pertanian di Bagian Negara Agraris. VIGOR: Jurnal Ilmu Pertanian Tropika dan Subtropika, 5(2), 38–44.

BPS Provinsi Kalimantan Timur. (2022). Statistik Nilai Tukar Petani Provinsi Kalimantan Timur 2022.

BPS Provinsi Kalimantan Timur. (2024). Berita Resmi Statistik No. 57/10/64/Th.XXVII, 1 Oktober 2024.

Chatfield, C., & Xing, H. (2019). The Analysis of Time Series : an Introduction with R Sevent Edition (7 ed.). CRC Press.

Dian, F., Supriyadi, D., & Fitriana, G. F. (2022). Penerapan Metode Jaringan Syaraf Tiruan Backpropagation untuk Prediksi Tingkat Kemiskinan di Provinsi Jawa Tengah. Jurnal Teknologi dan Ilmu Komputer (JTIIK), 9(August), 464–655. https://doi.org/10.25126/jtiik.202294806

Fausett, L. V. (1994). Fundamentals of Neural Networks : Architectures, Algorithms, and Applications. In Agricultural Systems modeting and Simulation. Prentice Hall. https://doi.org/10.1201/9781482269765-16

Julpan, Nababan, E. B., & Zarlis, M. (2015). Analisis Fungsi Aktivasi Sigmoid Biner dan Sigmoid Bipolar dalam Algoritma Bacpropagation pada Prediksi Kemampuan Siswa. Jurnal Teknovasi, 2(1), 103–116.

Kartalopouslos, S. V. (1996). Understanding Neural Network and Fuzzy Logic : Basic Concepts and Applications. Wiley-IEEE Press.

Khoiri, H. A. (2023). Analisis Deret Waktu Univariat. UNIPMA PRESS.

Maiyuriska, R. (2022). Penerapan Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Panen Gabah Padi. Jurnal Informatika Ekonomi Bisnis, 4, 28–33. https://doi.org/10.37034/infeb.v4i1.115

Setyaningrum, O. A., Zukhronah, E., & Handajani, S. S. (2023). Peramalan Nilai Tukar Petani (NTP) di Indonesia Menggunakan Metode Hibrida Singular Spectrum Analysis (SSA)-Seasonal Autotegressive Integrated Moving Average (SARIMA). Prosiding SENPIKA VI, 1, 254–266.

Suahati, A. F., Nurrahman, A. A., & Rukmana, O. (2022). Penggunaan Jaringan Syaraf Tiruan – Backpropagation dalam Memprediksi Jumlah Mahasiswa Baru. Jurnal Media Teknik dan Sistem Industri, 6(1), 21. https://doi.org/10.35194/jmtsi.v6i1.1589

Sukhbaatar, O., Usagawa, T., & Choimaa, L. (2019). An Articial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course. International Journal of Emerging Technologies in Learning (iJET), 14(19), 77–92.

Veri, J., Surmayanti, S., & Guslendra, G. (2022). Prediksi Harga Minyak Mentah Menggunakan Jaringan Syaraf Tiruan. MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 21(3), 503–512. https://doi.org/10.30812/matrik.v21i3.1382