Planting Pattern Modeling Based on Rainfall Prediction Using Backpropagation Artificial Neural Network (Case Study: BMKG Rainfall Data, Deli Serdang Regency)

The results of data analysis are known from the Agricultural Research and
Development Agency that the cropping pattern in Deli Serdang Regency was initially rice-rice
with changes in varieties, the cropping pattern changed to the rice-rice-rice pattern. The
continuous rice cropping pattern for some time eventually caused new problems, namely the
exploitation of rice pests (leafhoppers, Nephotettix Virescens, Orsealia Oryzae) and causing
crop failure. This exploitative rice pest is one of the causes of the decline in agricultural
productivity in the Deli Serdang Regency. The purpose of this study provides alternative
solutions to increase agricultural production in Deli Serdang with modeling cropping pattern
most profitable based on the placement of planting time that best suits the needs of rainfall in
Deli Serdang that predicted using Neural Network Backpropagation so it can be used as
guidelines in the utilization of agricultural land in Deli Serdang Regency. The model
Backpropagation best in this study is 12-2-1, with the learning rate best 0.08 and best
momentum 0.99 with Mean Square Erro testing is 0.0260. Based on the planting calendar and
cropping models obtained from rainfall predictions, the cropping patterns that can be applied in
Deli Serdang Regency are modeled, namely the cropping patterns of palawija-rice-rice,
palawija-rice-palawija, palawija-palawija-palawija, and palawija-palawija-rice