Open AccessModel Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Produksi Telur Ayam Petelur Berdasarkan Provinsi Di Indonesia
Author(s) -
Pipit Mutiara Putri,
Devi Monika,
Lulu Apriliani,
Solikhun Solikhun
Publication year - 2019
Publication title -
jurnal teknoinfo
Language(s) - English
Resource type - Journals
eISSN - 2615-224X
pISSN - 1693-0010
DOI - 10.33365/jti.v13i2.273
Subject(s) - livestock , agricultural science , product (mathematics) , production (economics) , computer science , indonesian , scale (ratio) , business , biology , geography , mathematics , ecology , economics , cartography , geometry , macroeconomics , linguistics , philosophy
Improving human resources cannot be achieved without adequate nutrition. To educate, strengthen and improve the achievements of Indonesian people, much depends on fulfilling good nutrition, especially animal protein such as meat, milk and eggs (Anonymous, 1990).Eggs are one product that can meet some of the nutritional needs of the community. These livestock products also have the potential to be developed optimally, because in addition to the price that is relatively cheap compared to other animal proteins, the business is also relatively easy and even though it is cultivated in small-scale businesses it can increase income and expand employment opportunities (Anonymous, 1994). The data used is data from the National Statistics Agency through the website www.bps.go.id. The data is data on the number of egg production of laying hens based on the provinces in 2010 to 2017. The algorithms used in this study are Artificial Neural Networks with the Backpropogation method. The input variables used are data for 2010 (X1), data for 2011 (X2), data for 2012 (X3), data for 2013 (X4), data for 2014 (X5) data for 2015 (X6) and data in 2016 (X7) with a training and testing architecture model of 4 architectures namely 7-4-1, 7-8-1, 7-16-1dan 7-32-1. Target data is taken from 2017 data. The output produced is the best pattern of ANN architecture. The best architectural model is 32-1 with MSE 0.0082336 and an accuracy rate of 96.88%. From this model, the prediction of egg production of laying hens is based on the province of each province in Indonesia.