PENERAPAN JARINGAN SYARAF TIRUAN UNTUK PERAMALAN PERMINTAAN KOMODITAS KARET DI PERMINTAAN KOMODITAS KARET DI PT. PERKEBUNAN NUSANTARA XII SURABAYA
ABSTRACT: Artificial neural
network (ANN) system, an information computing processor, is developed to
resemble the natural biological neural network system. Like the biological one,
the ANN system is able to learn, store, and process or to interpret the new
data input based on the previous set of values. Therefore the ANN system may be
used as a tool for forecasting. This research was run to forecast demand on the
rubber commodity, RSS-1, produced by an Indonesian government-owned estate
company. The ANN system was modeled as a multilayer feed forward network by
means of a back propagation as a learning algorithm. A sigmoid function was
used as the activating function, whereas the learning rate (lr), momentum (mc),
and maximum epochs 100.000 were added to accelerate the learning process. The
training results showed the developed ANN system worked very well. There
network operated on the structure of 2-5-4-1 with the respective value of lr =
0.01 and mc = 0.9. This structure was able to recognize the causal design
between the demand, the price and stocks of RSS-1 rubber at the company by
rationality of network learning results, known as MSE, at a level of 0.69%.
Keyword: Artificial neural
network, demand forecasting, rubber
Penulis: Imam Santoso, Usman
Effendi, Cicik Fauziya
Kode Jurnal: jppertaniandd070010
