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

Abstract

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.