Artificial neural network method for forecasting price of purebred chicken egg in East Java

Ni'matur Rohmah; Iid Mufaidah

doi:10.30762/f_m.v7i2.3798

Authors

Ni'matur Rohmah Universitas Muhammadiyah Jember
Iid Mufaidah Institut Teknologi dan Bisnis Muhammadiyah Banyuwangi

DOI:

https://doi.org/10.30762/f_m.v7i2.3798

Keywords:

Forecasting, Artificial Neural Network, Purebred Chicken Egg

Abstract

Purebred chicken eggs are a staple food source commodity that is widely chosen by the community because it has an affordable price with high protein content and sufficient availability. However, even though availability of purebred chicken eggs is sufficient, there is still a problem, namely very fluctuating prices. Efforts to read these uncertain market conditions are urgently needed for decision-making by producers, consumers, and even the government, one of which is by forecasting prices. Forecasting can be used to predict future conditions by looking at trends in past conditions. This study uses Artificial Neural Network method with a feed-forward backpropagation algorithm. The purpose of this study is to determine price forecasting architecture using ANN and forecast price of purebred chicken eggs in East Java in 2024 and 2025. The research data used is monthly price of purebred chicken eggs in East Java province from 2018 to 2024. The results of study produced best architecture, 12-12-1. The resulting training Mean Square Error value was 0.00099947 with an accuracy of 98.01%, MSE of the test was 0.023797 and MAPE value was 0.164254. Based on MAPE values generated by forecasting using Artificial Neural Network, it has a good level of forecasting ability.

Downloads

Download data is not yet available.

References

Alpaydin, E. (2020). Introduction to machine learning-4th edition. Cambridge: MIT Press.

Aprijani, D. A. (2011). Aplikasi jaringan syaraf tiruan untuk mengenali tulisan tangan huruf a, b, c, dan d pada jawaban soal pilihan ganda (studi eksplorasi pengembangan pengolahan lembar jawaban ujian soal pilihan ganda di Universitas Terbuka ). Jurnal Matematika, Saint dan Teknologi, 12(1), 11–17. https://jurnal.ut.ac.id/index.php/jmst/article/view/485.

Badan Pusat Statistik. (2021). Distribusi perdagangan komoditas telur ayam ras indonesia. https://doi.org/8201022.

Badan Pusat Statistik. (2023). Distribusi perdagangan komoditas telur ayam ras indonesia 2023. Retrieved from: https://www.bps.go.id/id/publication/2023/11/22/ 3585f0898795b4189a71666e/distribusi-perdagangan-komoditas-telur-ayam-ras-indonesia-2023.html

Chen, J. C., & Narala, N. H. R. (2017). Forecasting currency exchange rates via Feedforward Backpropagation Neural Network. Universal Journal of Mechanical Engineering, 5(3), 77–86. https://doi.org/10.13189/ujme.2017.050302.

Disperindag. (2024). Harga konsumen telur ayam ras provinsi jawa timur. https://siskaperbapo.jatimprov.go.id/harga.

Dongare, A. D., Kharde, R. R., & Kachare, A. D. (2012). Introduction to artificial neural network (ann) methods. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189–194. https://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.1082.1323&rep=rep1&type=pdf.

Dwinata, S, Y., Pramusintho, B., Firmansyah, F., & Hoesni, F. (2022). Model peramalan harga telur ayam ras di pasar tradisional dan modern kota jambi. Ekonomis: Journal of Economics and Business, 6(1), 372. https://doi.org/10.33087/ekonomis.v6i1.521.

Fausett, L. (1994). Fundamentals of Neural Networks (Architectures, Algorithms, and Applications). New Jersey: Prentice Hall.

Fauzi, F. A. A., Wulandari, S., & Avianto, D. (2024). Penerapan metode neural network berbasis web dalam prediksi harga telur ayam. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(6), 2686–2697. https://doi.org/10.30865/klik.v4i6.1865.

Hakim, I. L., Sanglise, M., & Suhendra, C. D. (2024). Analisis peramalan harga telur ayam ras dengan menggunakan Metode SARIMA. Jurnal Media Informatika Budidarma, 8(2), 966. https://doi.org/10.30865/mib.v8i2.7610.

Haykin, S. (1999). Neural network: a comperehensive foundation (2nd edition). Prentice Hall.

Ilham, N., & Saptana. (2019). Fluktuasi harga telur ayam ras dan faktor penyebabnya. Analisis Kebijakan Pertanian, 17(1), 27–38. https://epublikasi.pertanian.go.id/berkala/akp/article/view/824.

Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 531–538. https://doi.org/10.1002/sam.11583.

Kumare, S. T., Perke, D. S., & Rede, G. D. (2022). Market integration and seasonal prices of paddy: An economic analysis. Economic Affairs, 67(4), 407-413. https://www.doi.org/10.46852/0424-2513.4.2022.5.

Marzuqi, M. A. (2024). Analisis volatilitas harga komoditas telur ayam ras di provinsi jawa timur. Jurnal Agrica. 17(2). 152-161. https://doi.org/10.31289/agrica.v17i2.11865.

Mufaidah, I., Suwarsono, S., Wibowo, Y., & Soedibyo, D. W. (2017). Peramalan jumlah permintaan udang beku PND menggunakan metode jaringan syaraf tiruan (jst) backpropagation. Jurnal Agroteknologi. 11 (1). 17-22. https://www.academia.edu/download/81265097/4076.pdf.

Naidu, G. M., & Kala, M. S. (2016). A statistical study of trends in arrivals and prices of maize in selected markets of Andhra Pradesh. International Journal of Agricultural and Statistical Sciences, 12(1), 89-94. http://www.connectjournals.com/ijass.

Nurhayati, Y. Y. H. N. (2012). Variabilitas Harga Telur Ayam Ras di Indonesia. In Buletin Ilmiah Litbang Perdagangan (pp. 235–252).

Qiao, Y., & Ahn, B. I. (2024). Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions. Agribusiness. https://doi.org/10.1002/agr.21958.

Ramadhan, R. K., Rohman, D. L. A., & Indrayana, Y. K. (2024). Prediksi Harga Cabai Rawit Merah Tahun 2024 Menggunakan Jaringan Syaraf Tiruan Backpropagation Di Kabupaten Banjarnegara. In Prosiding Seminar Nasional Amikom Surakarta (Vol. 2, pp. 148-159).

Rinjani, S. N., Hoyyi, A., & Suparti, S. (2019). Pemodelan fungsi transfer dan backpropagation neural network untuk peramalan harga emas (studi kasus harga emas bulan juli 2007 sampai februari 2019). Jurnal Gaussian, 8(4), 474–485. https://doi.org/10.14710/j.gauss.v8i4.26727.

Siang, J. J. (2005). Jaringan syaraf tiruan dan pemprogramannya menggunakan matlab. Yogyakarta: Andi Offset.

Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological modelling, 220(6), 888-895. https://doi.org/10.1016/j.ecolmodel.2009.01.004.

Syamsiah, N. O. (2020). Peramalan harga telur ayam ras di jakarta timur berbasis jaringan syaraf tiruan. CESS (Journal of Computer Engineering, System and Science), 5(1), 65. https://doi.org/10.24114/cess.v5i1.15554.

Utnik-Banaś, K., Schwarz, T., Szymanska, E. J., Bartlewski, P. M., & Satoła, Ł. (2022). Scrutinizing pork price volatility in the European Union over the last decade. Animals, 12(1), 100. https://doi.org/10.3390/ani12010100.

Wibawa, M. S. (2017). Pengaruh fungsi aktivasi, optimisasi dan jumlah epoch terhadap performa jaringan saraf tiruan. Jurnal Sistem dan Informatika, 11(2), 167–174. https://jsi.stikom-bali.ac.id/index.php/jsi/article/view/129.

Yegnanarayana, B. (2009). Articial neural networks. New Delhi: Prentice-Hall India Learning Pvt. Ltd.

Zain, M. N., & Sirodj, D. A. N. (2022). Algoritma convolutional neural network dalam klasifikasi chest x-rays pasien covid-19 menggunakan fungsi aktivasi sigmoid. Statmat : Jurnal Statistika dan Matematika, 4(2), 156–165. https://doi.org/10.32493/sm.v4i2.27058.

Zou, J., Han, Y., & So, S.-S. (2009). Overview of artificial neural networks. In Artificial Neural Networks: Methods and Applications (D. J. Livingstone (ed.); pp. 14–22). Humana Press. https://doi.org/10.1007/978-1-60327-101-1_2.