APLIKASI JARINGAN SYARAF TIRUAN PADA SISTEM DETEKSI DINI UNTUK MANAJEMEN KRISIS PANGAN
Abstrak: Artificial neural
network (ANN) has widely used to various sectors in agriculture. In term of
food security management, ANN used to determine food crisis level based on its
factors. The aim of this research is to increase ANN performance in term of
pattern recognition by advanced learning using updated data as well as ANN
weight analysis. This research has used multi-layer perceptron 2 hidden layers
with backpropagation algorithm. The input-output patterns were food crisis
factors and crisis level, respectively. Result showed that advance learning
could increase accuracy level. It was from 70,55% to 85,38%. Based on weight
analysis of ANN neuron, factors that affected to crisis level were: (1) crop
failure/natural disaster, (2) normative consumption ratio, (3) rice price, (4)
stock exchange, (5) infant mortality, (6) non forest area, (7) currency, (8)
people under poverty line, (9) underweight infant and (10) annual rainfall. The
3 big factors are critical aspect should be concerned in food crisis
management.
Penulis: Rizky Mulya Sampurno,
Kudang B. Seminar
Kode Jurnal: jppertaniandd170509
