• Ahmad Haris Hasanuddin Slamet University of Jember
  • Bambang Herry Purnomo
  • Dedy Wirawan Soedibyo
Keywords: poultry feed, price of meat bone meal (MBM), backpropagation neural networks


XYZ is a poultry feed producer in Banyuwangi Regency, East Java. The problem in developing poultry feed at PT XYZ was the fluctuating price of poultry feed. Meat bone meal (MBM) or what is called meat flour is one of the raw materials for poultry feed that affects the final price of poultry feed products. The price of MBM was greatly influenced by the exchange rate of the rupiah against the dollar. Forecasting is one way that needs to be done in dealing with MBM price fluctuations. The aim of this study was to estimate the price of MBM using backpropagation neural networks (BNN). The data used in this study was the price of MBM in the period January 2016-October 2018. Based on the results of the study, the best BNN architecture for the estimated MBM price was12-10-1 (12 input nodes, 10 hidden nodes, and 1 output node). This architecture has reached the training target of 0.002 with a MAPE test value of 13.93%. Based on forecasts with the BNN the highest MBM price in May 2019 and the lowest MBM price in January 2019.


Alfi, N. A. (2018). Pakan Ternak: Konsumsi Diyakini Tumbuh 8%. Retrieved from

Azhar, M., Riksakomara, E., & Terkait, A. P. (2017). Peramalan Jumlah Produksi Ikan dengan Menggunakan Backpropagation Neural Network (Studi Kasus: UPTD Pelabuhan Perikanan Banjarmasin). Journal of Engineering ITS, 6(1), 142–148.

Boadi, P. O., Bondinuba, F. K., Meng, J., Shi, E., Li, J., Antwi, P., & Deng, K. (2016). Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresource Technology, 228, 106–115.

Cheng, J., Wang, X., Si, T., Zhou, F., Zhou, J., & Cen, K. (2016). Ignition temperature and activation energy of power coal blends predicted with back-propagation neural network models. Fuel, 173(January), 230–238.

Dinas Peternakan. (2019). Jangan Berikan MBM (Meat and Bone Meal) Sebagai Campuran Pakan Ternak Ruminansia. Retrieved from

Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34(3), 828–841.

Kusumadewi, S., & Hartati, S. (2006). Neuro Fuzzy: Integrasi Sistem Fuzzy Dan Jaringan Syaraf. Yogyakarta: Graha Ilmu.

Liu, S., Xu, L., & Li, D. (2016). Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. Computers and Electrical Engineering, 49, 1–8.

Siang, J. J. (2005). Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab. Yogyakarta: Andi.

Singhal, D., & Swarup, K. S. (2018). Electricity Prices Forecasting using Artificial Neural Networks. IEEE Latin America Transactions, 16(1), 105–111.

Yuniartha, L. (2018). Bungkil Kedelai Naik Berpotensi Naikkan Harga Pakan Ternak. Retrieved from

Zhao, Y., Nan, J., Cui, F., & Guo, L. (2007). Water quality forecast through application of BP neural network at Yuqiao reservoir. Journal of Zhejiang University-SCIENCE A, 8(9), 1482–1487.

Zou, H. F., Xia, G. P., Yang, F. T., & Wang, H. Y. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(16–18), 2913–2923.

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