Open AccessPRAKIRAAN HARGA MEAT BONE MEAL (MBM) MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION
Author(s) -
Ahmad Haris Hasanuddin Slamet,
Bambang Herry Purnomo,
Dedy Wirawan Soedibyo
Publication year - 2021
Publication title -
jurnal agribisnis/jurnal agribisnis
Language(s) - English
Resource type - Journals
eISSN - 2598-733X
pISSN - 2301-5330
DOI - 10.32520/agribisnis.v10i1.926
Subject(s) - meat and bone meal , backpropagation , liberian dollar , meal , raw material , agricultural science , business , mathematics , computer science , artificial neural network , food science , environmental science , biology , fish meal , fish <actinopterygii> , artificial intelligence , ecology , finance , fishery
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.